diff --git a/MLproject b/MLproject
index 84ee6be..81b4425 100755
--- a/MLproject
+++ b/MLproject
@@ -2,4 +2,4 @@ name: cell_segmentation_gff
 
 entry_points:
   train_model:
-    command: "python3 train.py"
+    command: "uv run train.py"
diff --git a/callbacks/Callbacks.py b/callbacks/Callbacks.py
index aeb991d..c8617e5 100644
--- a/callbacks/Callbacks.py
+++ b/callbacks/Callbacks.py
@@ -167,6 +167,7 @@ def _on_epoch_end(
             ("train", train_dataloader),
             ("validation", val_dataloader),
         ]:
+            f = 0
 
             self._log_epoch_metrics(
                 model=model,
@@ -179,9 +180,11 @@ def _on_epoch_end(
 
         # Images can be saved in different ways if desired in the future
         if self.image_savers is not None and not isinstance(self.image_savers, list):
-            self.image_savers(
-                dataset=val_dataloader.dataset.dataset, model=model, epoch=epoch
-            )
+            self.image_savers(model=model, epoch=epoch)
+
+        else:
+            for image_saver in self.image_savers:
+                image_saver(model=model, epoch=epoch)
 
         val_sample = next(iter(val_dataloader))
         val_sample = val_sample["input"]
diff --git a/callbacks/utils/SaveEpochSlices.py b/callbacks/utils/SaveEpochSlices.py
index 6bf6274..5ac22c5 100644
--- a/callbacks/utils/SaveEpochSlices.py
+++ b/callbacks/utils/SaveEpochSlices.py
@@ -7,6 +7,8 @@
 import tifffile
 import torch
 
+from .save_utils import save_image_mlflow
+
 
 class SaveEpochSlices:
     """
@@ -15,30 +17,24 @@ class SaveEpochSlices:
 
     def __init__(
         self,
-        image_dataset_idxs: list[int],
-        data_split: str,
+        image_dataset: torch.utils.data.Dataset,
         image_postprocessor: Any = lambda x: x,
+        image_dataset_idxs: Optional[list[int]] = None,
     ) -> None:
 
+        self.image_dataset = image_dataset
         self.image_dataset_idxs = image_dataset_idxs
-        self.data_split = data_split
         self.crop_key_order = ["height_start", "height_end", "width_start", "width_end"]
         self.image_postprocessor = image_postprocessor
 
-    def save_image_mlflow(
-        self,
-        image: torch.Tensor,
-        save_image_path_folder: str,
-        image_filename: str,
-    ) -> None:
-
-        with tempfile.TemporaryDirectory() as tmp_dir:
-            save_path = pathlib.Path(tmp_dir) / image_filename
-            tifffile.imwrite(save_path, image.astype(np.uint8))
+        self.epoch = None
+        self.metadata = None
 
-            mlflow.log_artifact(
-                local_path=save_path, artifact_path=save_image_path_folder
-            )
+        self.image_dataset_idxs = (
+            range(len(image_dataset))
+            if image_dataset_idxs is None
+            else image_dataset_idxs
+        )
 
     def save_image(
         self,
@@ -67,20 +63,18 @@ def save_image(
             for k in self.crop_key_order
         )
 
-        filename = (
-            f"{image_path.stem}__{image_type}{image_path.suffix}"
-            if image_type == "generated_prediction"
-            else image_path.name
-        )
+        image_suffix = ".tiff" if ".tif" in image_path.suffix else image_path.suffix
 
-        image_filename = f"{crop_name}__{filename}"
+        image_filename = (
+            f"3D_{image_type}_{image_path.stem}__{crop_name}__{image_suffix}"
+        )
 
         fov_well_name = image_path.parent.name
         patient_name = image_path.parents[2].name
 
-        save_image_path_folder = f"epoch_{self.epoch:02}/{patient_name}/{fov_well_name}/{input_slices_name}__{target_slices_name}"
+        save_image_path_folder = f"cropped_images/epoch_{self.epoch:02}/{patient_name}/{fov_well_name}/{input_slices_name}__{target_slices_name}"
 
-        self.save_image_mlflow(
+        save_image_mlflow(
             image=image,
             save_image_path_folder=save_image_path_folder,
             image_filename=image_filename,
@@ -93,12 +87,10 @@ def predict_target(
     ) -> torch.Tensor:
         return self.image_postprocessor(model(image.unsqueeze(0)).squeeze(0))
 
-    def __call__(
-        self, dataset: torch.utils.data.Dataset, model: torch.nn.Module, epoch: int
-    ) -> None:
+    def __call__(self, model: torch.nn.Module, epoch: int) -> None:
         self.epoch = epoch
         for sample_idx in self.image_dataset_idxs:
-            sample = dataset[sample_idx]
+            sample = self.image_dataset[sample_idx]
             self.metadata = sample["metadata"]
 
             sample_image = self.save_image(
@@ -121,6 +113,6 @@ def __call__(
 
                 self.save_image(
                     image_path=sample["target_path"],
-                    image_type="generated_prediction",
+                    image_type="generated-prediction",
                     image=generated_prediction,
                 )
diff --git a/callbacks/utils/SaveWholeSlices.py b/callbacks/utils/SaveWholeSlices.py
new file mode 100644
index 0000000..9b96d4f
--- /dev/null
+++ b/callbacks/utils/SaveWholeSlices.py
@@ -0,0 +1,232 @@
+import pathlib
+from typing import Any, Optional
+
+import numpy as np
+import pandas as pd
+import tifffile
+import torch
+
+from .image_padding_specs import compute_patch_mapping
+from .save_utils import save_image_locally, save_image_mlflow
+
+
+class SaveWholeSlices:
+    """
+    Saves chosen images, and all voxels from those images, to a 3D tiff format either locally or in MLflow.
+    """
+
+    def __init__(
+        self,
+        image_dataset: torch.utils.data.Dataset,
+        image_dataset_idxs: list[int],
+        image_specs: dict[str, Any],
+        stride: tuple[int],
+        crop_shape: tuple[int],
+        pad_mode="reflect",
+        image_postprocessor: Any = lambda x: x,
+        local_save_path: Optional[pathlib.Path] = None,
+    ):
+
+        self.image_dataset = image_dataset
+        self.image_dataset_idxs = image_dataset_idxs
+        self.image_specs = image_specs
+        self.stride = stride
+        self.crop_shape = crop_shape
+        self.pad_mode = pad_mode
+        self.image_postprocessor = image_postprocessor
+        self.local_save_path = local_save_path
+
+        self.unique_image_dataset_idxs = []
+        self.reduce_dataset_idxs(image_dataset=image_dataset)
+
+        self.pad_width, self.original_crop_coords = None, None
+        self.epoch = None
+
+    def reduce_dataset_idxs(self, image_dataset: torch.utils.data.Dataset):
+        """
+        For reducing the dataset to only unique indices.
+        We don't want to save redundant images.
+        Dataset indices reflect crop samples, and not whole image samples prior to this function.
+        """
+        self.unique_image_dataset_idxs = []
+
+        for sample_idx in self.image_dataset_idxs:
+            if (
+                image_dataset[sample_idx]["metadata"]["Metadata_ID"]
+                not in self.unique_image_dataset_idxs
+            ):
+                self.unique_image_dataset_idxs.append(sample_idx)
+
+    def predict_target(
+        self, padded_image: torch.Tensor, model: torch.nn.Module
+    ) -> torch.Tensor:
+        """
+        padded_image:
+            Expects image of shape: (Z, H, W)
+            Z -> Number of Z slices
+            H -> Image Height
+            W -> Image Width
+        """
+
+        output = torch.zeros(
+            *padded_image.shape,
+            dtype=torch.float32,
+            device=padded_image.device,
+        )
+        weight = torch.zeros_like(output)
+
+        spatial_ranges = [
+            range(0, s - c, st)
+            for s, c, st in zip(padded_image.shape, self.crop_shape, self.stride)
+        ]
+
+        for idx in torch.cartesian_prod(
+            *[torch.tensor(list(r)) for r in spatial_ranges]
+        ):
+            start = idx.tolist()
+            end = [s + c for s, c in zip(start, self.crop_shape)]
+
+            slices = tuple(slice(s, e) for s, e in zip(start, end))
+            crop = padded_image[slices].unsqueeze(0)  # add batch dim
+
+            with torch.no_grad():
+                generated_prediction = self.image_postprocessor(
+                    generated_prediction=model(crop)
+                ).squeeze(0)
+
+            output[slices] += generated_prediction
+            weight[slices] += 1.0
+
+        output /= weight
+
+        return output[self.original_crop_coords]
+
+    def pad_image(self, input_image: torch.Tensor) -> torch.Tensor:
+        """
+        input_image:
+            Expects image of shape: (Z, H, W)
+            Z -> Number of Z slices
+            H -> Image Height
+            W -> Image Width
+        """
+
+        padded_image = np.pad(
+            input_image.detach().cpu().numpy(),
+            pad_width=self.pad_width,
+            mode=self.pad_mode,
+        )
+
+        padded_image = torch.from_numpy(padded_image).to(
+            dtype=torch.float32, device=input_image.device
+        )
+
+        return padded_image
+
+    def save_image(
+        self,
+        image_path: pathlib.Path,
+        image_type: str,
+        image: torch.Tensor,
+    ) -> bool:
+        """
+        - Determines if the image is completely black or not.
+        - Saves images in the correct format to the hardcoded path.
+        """
+
+        if not ((image > 0.0) & (image < 1.0)).any():
+            if image_type == "input":
+                raise ValueError("Pixels should be between 0 and 1 in the input image")
+
+        if image_type == "target":
+            image = (image != 0).float()
+
+        image = (image * 255).byte().cpu().numpy()
+
+        # Black images will not be saved
+        if np.max(image) == 0:
+            return False
+
+        image_suffix = ".tiff" if ".tif" in image_path.suffix else image_path.suffix
+
+        filename = f"3D_{image_type}_{image_path.stem}{image_suffix}"
+
+        fov_well_name = image_path.parent.name
+        patient_name = image_path.parents[2].name
+
+        save_image_path_folder = f"{patient_name}/{fov_well_name}"
+        save_image_path_folder = (
+            f"whole_images/epoch_{self.epoch:02}/{save_image_path_folder}"
+            if self.epoch is not None
+            else save_image_path_folder
+        )
+
+        if self.local_save_path is None:
+            save_image_mlflow(
+                image=image,
+                save_image_path_folder=save_image_path_folder,
+                image_filename=filename,
+            )
+        else:
+            save_image_path_folder = self.local_save_path / save_image_path_folder
+            save_image_locally(
+                image=image,
+                save_image_path_folder=save_image_path_folder,
+                image_filename=filename,
+            )
+
+        return True
+
+    def __call__(
+        self,
+        model: torch.nn.Module,
+        epoch: Optional[int] = None,
+    ) -> None:
+
+        self.epoch = epoch
+        for sample_idx in self.unique_image_dataset_idxs:
+
+            self.image_specs["image_shape"][0] = tifffile.imread(
+                self.image_dataset[sample_idx]["input_path"]
+            ).shape[0]
+
+            # For computing image padding and original crop coordinates
+            # Only the z-padding and the z-crop coordinates need to be computed
+            # each time, because the number of z-slices isn't consistent across
+            # 3D images.
+            self.pad_width, self.original_crop_coords = compute_patch_mapping(
+                image_specs=self.image_specs,
+                crop_shape=self.crop_shape,
+                stride=self.stride,
+                pad_slices=True,
+            )
+
+            sample_image = self.save_image(
+                image_path=self.image_dataset[sample_idx]["target_path"],
+                image_type="target",
+                image=self.image_dataset[sample_idx]["target"],
+            )
+
+            # Only save these images if the segmentation mask isn't black
+            # We expect the model to generate black segmentation crops,
+            # which will present regardless of weather or not the whole segmented image
+            # is black or not.
+            if sample_image:
+                padded_image = self.pad_image(
+                    input_image=self.image_dataset[sample_idx]["input"]
+                )
+
+                generated_prediction = self.predict_target(
+                    padded_image=padded_image, model=model
+                )
+
+                self.save_image(
+                    image_path=self.image_dataset[sample_idx]["input_path"],
+                    image_type="input",
+                    image=self.image_dataset[sample_idx]["input"],
+                )
+
+                self.save_image(
+                    image_path=self.image_dataset[sample_idx]["target_path"],
+                    image_type="generated-prediction",
+                    image=generated_prediction,
+                )
diff --git a/callbacks/utils/image_padding_specs.py b/callbacks/utils/image_padding_specs.py
new file mode 100644
index 0000000..2c8a650
--- /dev/null
+++ b/callbacks/utils/image_padding_specs.py
@@ -0,0 +1,89 @@
+import pathlib
+from typing import Any, Optional
+
+import numpy as np
+import tifffile
+
+
+def compute_pad(
+    image_dim_size: int, crop_dim_size: int, stride: int
+) -> tuple[int, int]:
+    """Compute how much padding is needed for one dimension."""
+    if image_dim_size <= crop_dim_size:
+        raise ValueError(
+            "The crop dimensions must be smaller than the image dimensions."
+        )
+
+    remainder = (image_dim_size - crop_dim_size) % stride
+    total_pad = stride - remainder
+
+    return total_pad // 2, total_pad - total_pad // 2
+
+
+def compute_patch_mapping(
+    image_specs: dict[str, Any],
+    crop_shape: tuple[int],
+    stride: tuple[int],
+    pad_slices: bool = True,
+) -> tuple[tuple[tuple[int, int], ...], tuple[slice, ...]]:
+    """
+    Compute the reflection padding required to evenly tile a 3D image
+    with patches of the given crop size and stride. Also returns the
+    bounding box coordinates to recover the original image region
+    after padding.
+
+    Parameters
+    ----------
+    image_specs : dict[str, Any]
+        Dictionary containing image dimensions with the key:
+        - "image_shape": tuple[int]
+            Expects Image shape to be: (Z, H, W)
+            Z -> Number of Z slices
+            H -> Image Height
+            W -> Image Width
+
+    crop_shape : tuple[int]
+        Expects the crop shape to be: (Z, H, W)
+        Z -> Number of Z slices
+        H -> Image Height
+        W -> Image Width
+
+    stride : tuple[int]
+        Expects stride shapeto be: (Z, H, W)
+        Z -> Number of Z slices
+        H -> Stride Height
+        W -> Stride Width
+
+    pad_slices: bool
+        Whether to pad the z-slices dimension or not.
+
+    Returns
+    -------
+    tuple
+        A tuple of two elements:
+        - Padding Amounts: ((z_before, z_after), (h_before, h_after), (w_before, w_after))
+            Amount of reflection padding applied to each dimension.
+        - Original Crop Coordinates: (slice_z, slice_h, slice_w)
+            Slice objects that can be used to recover
+            the original unpadded image from the padded one.
+    """
+
+    padding_slices = tuple([crop_shape[0] // 2] * 2) if pad_slices else (0, 0)
+
+    padding_height = compute_pad(
+        image_dim_size=image_specs["image_shape"][1],
+        crop_dim_size=crop_shape[1],
+        stride=stride[1],
+    )
+
+    padding_width = compute_pad(
+        image_dim_size=image_specs["image_shape"][2],
+        crop_dim_size=crop_shape[2],
+        stride=stride[2],
+    )
+
+    return (padding_slices, padding_height, padding_width), (
+        slice(padding_slices[0], image_specs["image_shape"][0] + padding_slices[0]),
+        slice(padding_height[0], image_specs["image_shape"][1] + padding_height[0]),
+        slice(padding_width[0], image_specs["image_shape"][2] + padding_width[0]),
+    )
diff --git a/callbacks/utils/save_utils.py b/callbacks/utils/save_utils.py
new file mode 100644
index 0000000..8ff3a3f
--- /dev/null
+++ b/callbacks/utils/save_utils.py
@@ -0,0 +1,31 @@
+import pathlib
+import tempfile
+
+import mlflow
+import numpy as np
+import tifffile
+import torch
+
+
+def save_image_mlflow(
+    image: torch.Tensor,
+    save_image_path_folder: str,
+    image_filename: str,
+) -> None:
+
+    with tempfile.TemporaryDirectory() as tmp_dir:
+        save_path = pathlib.Path(tmp_dir) / image_filename
+        tifffile.imwrite(save_path, image.astype(np.uint8))
+
+        mlflow.log_artifact(local_path=save_path, artifact_path=save_image_path_folder)
+
+
+def save_image_locally(
+    image: torch.Tensor,
+    save_image_path_folder: str,
+    image_filename: str,
+) -> None:
+
+    save_image_path_folder = pathlib.Path(save_image_path_folder)
+    save_image_path_folder.mkdir(parents=True, exist_ok=True)
+    tifffile.imwrite(save_image_path_folder / image_filename, image.astype(np.uint8))
diff --git a/datasets/dataset_00/CellSlicetoSliceDataset.py b/datasets/dataset_00/CellSlicetoSliceDataset.py
index fc784a6..2606ec8 100644
--- a/datasets/dataset_00/CellSlicetoSliceDataset.py
+++ b/datasets/dataset_00/CellSlicetoSliceDataset.py
@@ -17,72 +17,19 @@ class CellSlicetoSliceDataset(Dataset):
 
     def __init__(
         self,
-        root_data_path: pathlib.Path,
-        patient_folders: list[str],
+        image_paths: list[pathlib.Path],
+        image_specs: dict[str, Any],
         image_selector: Any,
         image_preprocessor: Any,
     ):
-        self.root_data_path = root_data_path
-        self.patient_folders = patient_folders
 
-        self.data_paths = self.get_image_paths()
-        image_specs = self.get_image_specs()
         image_selector.set_image_specs(**image_specs)
-        self.data_crops = image_selector(self.data_paths)
+        self.data_crops = image_selector(image_paths)
         self.image_preprocessor = image_preprocessor
         self.image_preprocessor.set_image_specs(**image_specs)
 
         self.split_data = False
 
-    def get_image_paths(self):
-        """
-        Get the image path of patients.
-        """
-
-        root_dir = pathlib.Path(self.root_data_path)
-
-        image_mask_pairs = []
-
-        for patient in self.patient_folders:
-            sample_path = root_dir / patient
-
-            brightfield_paths = sample_path.rglob(
-                "profiling_input_images/**/*TRANS.tif"
-            )
-
-            for bright_path in brightfield_paths:
-                mask_path = bright_path.with_name("cell_masks.tiff")
-
-                if mask_path.exists():
-                    image_mask_pairs.append(
-                        {"input_path": bright_path, "target_path": mask_path}
-                    )
-
-        return image_mask_pairs
-
-    def get_image_specs(self) -> None:
-
-        input_example = tifffile.imread(self.data_paths[0]["input_path"])
-        target_example = tifffile.imread(self.data_paths[0]["target_path"])
-
-        if target_example.ndim == 2:
-            image_height, image_width = target_example.shape
-        elif target_example.ndim == 3:
-            image_height, image_width = (
-                target_example.shape[1],
-                target_example.shape[2],
-            )
-        else:
-            raise ValueError(f"Unexpected target shape: {target_example.shape}")
-
-        return {
-            "input_max_pixel_value": np.iinfo(input_example.dtype).max,
-            "input_ndim": input_example.ndim,
-            "target_ndim": target_example.ndim,
-            "image_height": image_height,
-            "image_width": image_width,
-        }
-
     def __len__(self):
         return len(self.data_crops)
 
diff --git a/datasets/dataset_00/utils/ImagePreProcessor.py b/datasets/dataset_00/utils/ImagePreProcessor.py
index 3eab1b0..cf7d1bd 100644
--- a/datasets/dataset_00/utils/ImagePreProcessor.py
+++ b/datasets/dataset_00/utils/ImagePreProcessor.py
@@ -12,12 +12,12 @@ class ImagePreProcessor:
 
     def __init__(
         self,
-        pad_to_multiple: int,
+        image_specs: dict[str, Any],
         device: str = "cuda",
         input_transform: Optional[callable] = None,
         target_transform: Optional[callable] = None,
     ):
-        self.pad_to_multiple = pad_to_multiple
+        self.image_specs = image_specs
         self.device = device
         self.input_transform = input_transform
         self.target_transform = target_transform
@@ -73,12 +73,12 @@ def __call__(
         if self.target_transform is not None:
             target_img = self.target_transform(image=target_img)["image"]
 
-        input_img = input_img / self.input_max_pixel_value
+        input_img = input_img / self.image_specs["input_max_pixel_value"]
 
         target_img = (target_img != 0).astype(np.float32)
 
-        input_img = self.format_img(input_img, self.input_ndim)
-        target_img = self.format_img(target_img, self.target_ndim)
+        input_img = self.format_img(input_img, self.image_specs["input_ndim"])
+        target_img = self.format_img(target_img, self.image_specs["target_ndim"])
 
         processed_data = {
             "input_image": input_img,
diff --git a/datasets/dataset_00/utils/ImageSelector.py b/datasets/dataset_00/utils/ImageSelector.py
index caf1622..33d2ee5 100644
--- a/datasets/dataset_00/utils/ImageSelector.py
+++ b/datasets/dataset_00/utils/ImageSelector.py
@@ -14,38 +14,75 @@ class ImageSelector:
 
     def __init__(
         self,
-        number_of_slices: int = 1,
+        input_crop_shape: tuple[int],
+        target_crop_shape: tuple[int],
         slice_stride: int = 1,
         crop_stride: int = 256,
-        crop_height: int = 256,
-        crop_width: int = 256,
         device: Union[str, torch.device] = "cuda",
     ):
-        self.number_of_slices = number_of_slices
+        """
+        input_crop_shape:
+            Expects the crop shape to be: (Z, H, W)
+            Z -> Number of Z slices
+            H -> Image Height
+            W -> Image Width
+
+        target_crop_shape:
+            Expects image of shape: (Z, H, W)
+            Z -> Number of Z slices
+            H -> Image Height
+            W -> Image Width
+
+        slice_stride: int
+            Number of slices to move by when selecting images.
+
+        crop_stride: int
+            Size of the the stride in both height and width to move by when selecting images.
+        """
+
+        if len(input_crop_shape) != 3 or len(target_crop_shape) != 3:
+            raise ValueError(
+                "The input and target must both have three dimensions of (z-slices, height, width)"
+            )
+
+        for dimension in range(1, 3):
+            if input_crop_shape[dimension] != target_crop_shape[dimension]:
+                raise ValueError(
+                    "The height and width of both the input and target crops must be equal."
+                )
+
+        self.input_crop_shape = input_crop_shape
+        self.target_crop_shape = target_crop_shape
+
         self.slice_stride = slice_stride
         self.crop_stride = crop_stride
-        self.crop_height = crop_height
-        self.crop_width = crop_width
 
-        self.neighbors_per_side = self.number_of_slices // 2
+        self.input_neighbors_per_side = self.input_crop_shape[0] // 2
+        self.target_neighbors_per_side = self.target_crop_shape[0] // 2
         self.device = device
 
-        if self.number_of_slices % 2 == 0:
+        if self.input_crop_shape[0] % 2 == 0:
             raise ValueError("The model only accepts an odd number of slices")
 
     def set_image_specs(
         self,
         input_max_pixel_value: int,
-        image_height,
-        image_width,
+        image_shape: tuple[int],
         **kwargs,
     ) -> None:
+        self.image_shape = image_shape
         self.input_max_pixel_value = input_max_pixel_value
-        self.image_height = image_height
-        self.image_width = image_width
+
+    """
+    image_shape:
+        Expects image of shape: (Z, H, W)
+        Z -> Number of Z slices
+        H -> Image Height
+        W -> Image Width
+    """
 
     def select_zslices(
-        self, img: np.ndarray, img_path: pathlib.Path
+        self, img: np.ndarray, img_path: pathlib.Path, neighbors_per_side: int
     ) -> dict[pathlib.Path, list[dict[str, Any]]]:
         """
         Expects image of shape: (Z, H, W)
@@ -57,16 +94,16 @@ def select_zslices(
         zslice_groups = defaultdict(list)
 
         for z_index in range(
-            self.neighbors_per_side,
-            img.shape[0] - self.neighbors_per_side,
+            neighbors_per_side,
+            img.shape[0] - neighbors_per_side,
             self.slice_stride,
         ):
 
             selected_zslices = []
 
             for z_index_neigh in range(
-                z_index - self.neighbors_per_side,
-                z_index + self.neighbors_per_side + 1,
+                z_index - neighbors_per_side,
+                z_index + neighbors_per_side + 1,
             ):
                 selected_zslices.append(z_index_neigh)
 
@@ -148,19 +185,19 @@ def generate_crop_coords(self):
 
         crop_coords = []
         y_starts = list(
-            range(0, self.image_height - self.crop_stride + 1, self.crop_stride)
+            range(0, self.image_shape[1] - self.crop_stride + 1, self.crop_stride)
         )
         x_starts = list(
-            range(0, self.image_width - self.crop_stride + 1, self.crop_stride)
+            range(0, self.image_shape[2] - self.crop_stride + 1, self.crop_stride)
         )
         for y in y_starts:
             for x in x_starts:
                 crop_coords.append(
                     {
                         "height_start": y,
-                        "height_end": y + self.crop_height,
+                        "height_end": y + self.input_crop_shape[1],
                         "width_start": x,
-                        "width_end": x + self.crop_width,
+                        "width_end": x + self.input_crop_shape[2],
                     }
                 )
         return crop_coords
@@ -191,6 +228,7 @@ def __call__(
                 tifffile.imread(img_path["input_path"]).astype(np.float32)
                 / self.input_max_pixel_value,
                 img_path["input_path"],
+                neighbors_per_side=self.input_neighbors_per_side,
             )
 
             target_img = tifffile.imread(img_path["target_path"])
@@ -199,6 +237,7 @@ def __call__(
             z_slices_target = self.select_zslices(
                 target_img,
                 img_path["target_path"],
+                neighbors_per_side=self.target_neighbors_per_side,
             )
 
             data_locations.append({"input": z_slices_input, "target": z_slices_target})
diff --git a/datasets/dataset_00/utils/image_metadata.py b/datasets/dataset_00/utils/image_metadata.py
new file mode 100644
index 0000000..79dbc60
--- /dev/null
+++ b/datasets/dataset_00/utils/image_metadata.py
@@ -0,0 +1,50 @@
+import pathlib
+from typing import Any
+
+import numpy as np
+import tifffile
+
+
+def get_image_paths(
+    patient_folders: list[pathlib.Path],
+) -> list[dict[str, pathlib.Path]]:
+    """
+    Get the image path of patients.
+    """
+
+    image_mask_pairs = []
+
+    for patient in patient_folders:
+
+        brightfield_paths = patient.rglob("profiling_input_images/**/*TRANS.tif")
+
+        for bright_path in brightfield_paths:
+            mask_path = bright_path.with_name("cell_masks.tiff")
+
+            if mask_path.exists():
+                image_mask_pairs.append(
+                    {"input_path": bright_path, "target_path": mask_path}
+                )
+
+    return image_mask_pairs
+
+
+def get_image_specs(image_paths: list[dict[str, pathlib.Path]]) -> dict[str, Any]:
+
+    input_example = tifffile.imread(image_paths[0]["input_path"])
+    target_example = tifffile.imread(image_paths[0]["target_path"])
+
+    if target_example.ndim == 2:
+        image_height, image_width = target_example.shape
+        image_slices = None
+    elif target_example.ndim == 3:
+        image_slices, image_height, image_width = target_example.shape
+    else:
+        raise ValueError(f"Unexpected target shape: {target_example.shape}")
+
+    return {
+        "input_max_pixel_value": np.iinfo(input_example.dtype).max,
+        "input_ndim": input_example.ndim,
+        "target_ndim": target_example.ndim,
+        "image_shape": [image_slices, image_height, image_width],
+    }
diff --git a/datasets/dataset_01/AllSlicesDataset.py b/datasets/dataset_01/AllSlicesDataset.py
new file mode 100644
index 0000000..7da0fa7
--- /dev/null
+++ b/datasets/dataset_01/AllSlicesDataset.py
@@ -0,0 +1,69 @@
+import pathlib
+from typing import Any, Iterable, Optional, Union
+
+import numpy as np
+import tifffile
+import torch
+from albumentations import ImageOnlyTransform
+from PIL import Image
+from torch.utils.data import Dataset
+
+
+class AllSlicesDataset(Dataset):
+    """
+    Iterable Dataset for all slices of brightfield and masked Images,
+    which support image selection and preprocessing.
+    """
+
+    def __init__(
+        self,
+        dataset: torch.utils.data.Dataset,
+        image_specs: dict[str, Any],
+        image_preprocessor: Any,
+    ):
+
+        self.dataset = dataset
+        self.image_preprocessor = image_preprocessor
+        self.image_preprocessor.set_image_specs(**image_specs)
+
+        # Don't load images, just process the metadata
+        self.dataset.split_data = True
+        self.split_data = False
+
+        self.useless_metadata = [
+            "Metadata_Input_Slices",
+            "Metadata_Target_Slices",
+            "Metadata_Crop_Coordinates",
+            "Metadata_Sample_ID",
+        ]
+
+    def __getitem__(self, _idx: int):
+        """Returns input and target data pairs."""
+
+        sample = self.dataset[_idx]
+
+        for meta in self.useless_metadata:
+            sample["metadata"].pop(meta, None)
+
+        # Ensure only the data for splitting is returned rather than loading each image
+        if self.split_data:
+            return sample
+
+        input_image = tifffile.imread(sample["input_path"]).astype(np.float32)
+
+        target_image = tifffile.imread(sample["target_path"]).astype(np.float32)
+
+        self.processing_data = self.image_preprocessor(
+            input_img=input_image, target_img=target_image
+        )
+        input_image = self.processing_data.pop("input_image")
+        target_image = self.processing_data.pop("target_image")
+
+        return {
+            "input": input_image,
+            "target": target_image,
+            "metadata": sample["metadata"],
+            "processing_metadata": self.processing_data, # Should include stride and final crop coordinates
+            "input_path": sample["input_path"],
+            "target_path": sample["target_path"],
+        }
diff --git a/datasets/dataset_01/utils/ImagePostProcessor.py b/datasets/dataset_01/utils/ImagePostProcessor.py
new file mode 100644
index 0000000..b9f2754
--- /dev/null
+++ b/datasets/dataset_01/utils/ImagePostProcessor.py
@@ -0,0 +1,19 @@
+from typing import Any, Optional, Tuple, Union
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from torch.amp import autocast
+
+
+class ImagePostProcessor:
+    """
+    Processes generated predictions computed from the model.
+    """
+
+    def __init__(self):
+        pass
+
+    def __call__(self, generated_prediction: torch.Tensor) -> torch.Tensor:
+
+        return torch.sigmoid(generated_prediction)
diff --git a/datasets/dataset_01/utils/ImagePreProcessor.py b/datasets/dataset_01/utils/ImagePreProcessor.py
new file mode 100644
index 0000000..0fe1788
--- /dev/null
+++ b/datasets/dataset_01/utils/ImagePreProcessor.py
@@ -0,0 +1,80 @@
+from typing import Any, Optional, Tuple, Union
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+
+
+class AllSlicesImagePreProcessor:
+    """
+    Processes the image prior to model training and inferencing
+    """
+
+    def __init__(
+        self,
+        image_specs: dict[str, Any],
+        crop_slices: int,
+        crop_height: int,
+        crop_width: int,
+        device: str = "cuda",
+        input_transform: Optional[callable] = None,
+        target_transform: Optional[callable] = None,
+        pad_type: str = "reflection",
+    ):
+        self.device = device
+        self.input_transform = input_transform
+        self.target_transform = target_transform
+        self.pad_type = pad_type
+
+    def format_img(self, img: np.ndarray, img_dims: int) -> torch.Tensor:
+        """
+        Formats an image base on the number of image dimensions.
+        img can be either of the following dimensions:
+        (Z, H, W)
+        (H, W)
+        where:
+        Z: Number of Z-slices
+        H: Height of the image (in pixels)
+        W: Width of the image (in pixels)
+        """
+
+        if img_dims != 3:
+            raise ValueError(
+                f"The number of dimensions in your image should be 2 or 3. It is currently {img_dims}"
+            )
+        img = torch.from_numpy(img)
+
+        return img.to(dtype=torch.float32, device=self.device)
+
+    def __call__(
+        self, input_img: np.ndarray, target_img: np.ndarray = None
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Tuple[int, int, int, int]]:
+        """
+        Both images can be either of the following dimensions:
+        (Z, H, W)
+        (H, W)
+        where:
+        Z: Number of Z-slices
+        H: Height of the image (in pixels)
+        W: Width of the image (in pixels)
+        """
+
+        if self.input_transform is not None:
+            input_img = self.input_transform(image=input_img)["image"]
+
+        if self.target_transform is not None:
+            target_img = self.target_transform(image=target_img)["image"]
+
+        input_img = input_img / self.image_specs["input_max_pixel_value"]
+
+        target_img = (target_img != 0).astype(np.float32)
+
+        input_img = self.format_img(input_img, self.image_specs["input_ndim"])
+        target_img = self.format_img(target_img, self.image_specs["target_ndim"])
+
+        processed_data = {
+            "input_image": input_img,
+            "target_image": target_img,
+        }
+
+        return processed_data
diff --git a/datasets/dataset_01/utils/image_slice_selection.py b/datasets/dataset_01/utils/image_slice_selection.py
new file mode 100644
index 0000000..c2cdbb7
--- /dev/null
+++ b/datasets/dataset_01/utils/image_slice_selection.py
@@ -0,0 +1,17 @@
+def select_unique_image_idxs(image_dataset_idxs, image_dataset):
+    """
+    Identify redundant indices in the original dataset.
+    This dataset contains FOV crops as samples, whereas I want samples containing whole images
+    from multiple z-slices from the other dataset.
+    """
+
+    unique_image_dataset_idxs = []
+
+    for sample_idx in image_dataset_idxs:
+        if (
+            image_dataset[sample_idx]["metadata"]["Metadata_ID"]
+            not in unique_image_dataset_idxs
+        ):
+            unique_image_dataset_idxs.append(sample_idx)
+
+    return unique_image_dataset_idxs
diff --git a/pyproject.toml b/pyproject.toml
index 75d8eae..2671af0 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -6,16 +6,18 @@ readme = "README.md"
 requires-python = ">=3.11"
 dependencies = [
     "albumentations>=2.0.8",
-    "cityhash>=0.4.9",
-    "ipykernel>=6.29.5",
-    "matplotlib>=3.10.3",
-    "mlflow>=3.2.0",
+    "cityhash>=0.4.10",
+    "cuda-python==12.9",
+    "ipykernel>=7.0.1",
+    "matplotlib>=3.10.7",
+    "mlflow>=3.4.0",
     "nbconvert>=7.16.6",
-    "numpy>=2.3.1",
-    "optuna>=4.4.0",
-    "pynvim>=0.5.2",
-    "tifffile>=2025.6.11",
-    "torch>=2.7.1",
+    "numpy>=2.3.4",
+    "optuna>=4.5.0",
+    "pynvim>=0.6.0",
+    "tifffile>=2025.10.4",
+    "torch>=2.9.0",
+    "torchvision>=0.24.0",
 ]
 
 [project.scripts]
diff --git a/train.py b/train.py
index 101127a..249092a 100644
--- a/train.py
+++ b/train.py
@@ -11,10 +11,14 @@
 from callbacks.Callbacks import Callbacks
 from callbacks.utils.SampleImages import SampleImages
 from callbacks.utils.SaveEpochSlices import SaveEpochSlices
+from callbacks.utils.SaveWholeSlices import SaveWholeSlices
 from datasets.dataset_00.CellSlicetoSliceDataset import CellSlicetoSliceDataset
+from datasets.dataset_00.utils.image_metadata import (get_image_paths,
+                                                      get_image_specs)
 from datasets.dataset_00.utils.ImagePostProcessor import ImagePostProcessor
 from datasets.dataset_00.utils.ImagePreProcessor import ImagePreProcessor
 from datasets.dataset_00.utils.ImageSelector import ImageSelector
+from datasets.dataset_01.AllSlicesDataset import AllSlicesDataset
 from metrics.BCE import BCE
 from metrics.ConfusionMetrics import ConfusionMetrics
 from metrics.Dice import Dice
@@ -98,36 +102,15 @@ def __call__(self, trial):
             return trainer_obj.best_loss_value
 
 
-# |%%--%%| <Twmb6mPjf5|sNNHe7R4s3>
-r"""°°°
-## Find the root of the git repo on the host system
-°°°"""
-# |%%--%%| <sNNHe7R4s3|5uHY3JoEKK>
-
-# Get the current working directory
-cwd = pathlib.Path.cwd()
-
-if (cwd / ".git").is_dir():
-    root_dir = cwd
-
-else:
-    root_dir = None
-    for parent in cwd.parents:
-        if (parent / ".git").is_dir():
-            root_dir = parent
-            break
-
-# Check if a Git root directory was found
-if root_dir is None:
-    raise FileNotFoundError("No Git root directory found.")
-
-# |%%--%%| <5uHY3JoEKK|ExjIoeHzuw>
+# |%%--%%| <Twmb6mPjf5|ExjIoeHzuw>
 r"""°°°
 # Inputs
 °°°"""
 # |%%--%%| <ExjIoeHzuw|uHCF3KHHYz>
 
-root_data_path = root_dir / "big_drive/NF1_organoid_processed_patients"
+root_data_path = pathlib.Path("big_drive/NF1_organoid_processed_patients").resolve(
+    strict=True
+)
 patient_folders = [p for p in root_data_path.iterdir() if p.is_dir()]
 
 # |%%--%%| <uHCF3KHHYz|9NUAycuR83>
@@ -139,7 +122,6 @@ def __call__(self, trial):
 
 mlflow.log_param("random_seed", 0)
 
-
 description = """
 Optimization of the first segmentation model with the following:
 - UNet Generator
@@ -151,30 +133,43 @@ def __call__(self, trial):
 """
 mlflow.set_tag("mlflow.note.content", description)
 
-# |%%--%%| <9NUAycuR83|muTDx2W917>
+# |%%--%%| <9NUAycuR83|RXyUovWJFX>
+
+image_paths = get_image_paths(patient_folders=patient_folders)
+image_specs = get_image_specs(image_paths=image_paths)
+
+# |%%--%%| <RXyUovWJFX|muTDx2W917>
+
+input_crop_shape = (3, 512, 512)
 
 img_selector = ImageSelector(
-    number_of_slices=1,
+    input_crop_shape=input_crop_shape,
+    target_crop_shape=(1, 512, 512),
     slice_stride=1,
-    crop_stride=256,
-    crop_height=256,
-    crop_width=256,
+    crop_stride=512,
     device=device,
 )
-image_preprocessor = ImagePreProcessor(pad_to_multiple=16, device=device)
+
+image_preprocessor = ImagePreProcessor(image_specs=image_specs, device=device)
 image_postprocessor = ImagePostProcessor()
 
-img_dataset = CellSlicetoSliceDataset(
-    root_data_path=root_data_path,
-    patient_folders=patient_folders,
+crop_image_dataset = CellSlicetoSliceDataset(
+    image_paths=image_paths,
+    image_specs=image_specs,
     image_selector=img_selector,
     image_preprocessor=image_preprocessor,
 )
 
+whole_image_dataset = AllSlicesDataset(
+    dataset=crop_image_dataset,
+    image_specs=image_specs,
+    image_preprocessor=image_preprocessor,
+)
+
 # |%%--%%| <muTDx2W917|Ljn54YK9d8>
 
 hash_splitter = HashSplitter(
-    dataset=img_dataset,
+    dataset=crop_image_dataset,
     train_frac=0.8,
     val_frac=0.1,
 )
@@ -184,30 +179,40 @@ def __call__(self, trial):
 
 image_dataset_idxs = SampleImages(dataloader=val_dataloader, number_of_images=300)()
 
-image_saver = SaveEpochSlices(
+whole_image_saver = SaveWholeSlices(
+    image_dataset=whole_image_dataset,
     image_dataset_idxs=image_dataset_idxs,
-    data_split="validation",
+    image_specs=image_specs,
+    stride=(1, 32, 32),
+    crop_shape=input_crop_shape,
+    pad_mode="reflect",
     image_postprocessor=image_postprocessor,
 )
 
+image_prediction_saver = SaveEpochSlices(
+    image_dataset=val_dataloader.dataset.dataset,
+    image_postprocessor=image_postprocessor,
+    image_dataset_idxs=image_dataset_idxs,
+)
+
 # |%%--%%| <Ljn54YK9d8|sv6R19116h>
 
 callbacks_args = {
     "early_stopping_counter_threshold": 5,
-    "image_savers": image_saver,
+    "image_savers": [image_prediction_saver, whole_image_saver],
     "image_postprocessor": image_postprocessor,
 }
 
 # |%%--%%| <sv6R19116h|1ibSiDMEcz>
 
-unet = UNet(in_channels=1, out_channels=1)
+unet = UNet(in_channels=3, out_channels=1)
 
 # |%%--%%| <1ibSiDMEcz|yAnz5nSUyL>
 
 optimization_manager = OptimizationManager(
     trainer=UNetTrainer,
     hash_splitter=hash_splitter,
-    dataset=img_dataset,
+    dataset=crop_image_dataset,
     callbacks_args=callbacks_args,
     model=unet,
     epochs=30,
diff --git a/uv.lock b/uv.lock
index 867a969..410b660 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,5 +1,5 @@
 version = 1
-revision = 2
+revision = 3
 requires-python = ">=3.11"
 resolution-markers = [
     "python_full_version >= '3.12' and sys_platform == 'darwin'",
@@ -44,16 +44,16 @@ wheels = [
 
 [[package]]
 name = "alembic"
-version = "1.16.4"
+version = "1.17.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "mako" },
     { name = "sqlalchemy" },
     { name = "typing-extensions" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/83/52/72e791b75c6b1efa803e491f7cbab78e963695e76d4ada05385252927e76/alembic-1.16.4.tar.gz", hash = "sha256:efab6ada0dd0fae2c92060800e0bf5c1dc26af15a10e02fb4babff164b4725e2", size = 1968161, upload-time = "2025-07-10T16:17:20.192Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/6b/45/6f4555f2039f364c3ce31399529dcf48dd60726ff3715ad67f547d87dfd2/alembic-1.17.0.tar.gz", hash = "sha256:4652a0b3e19616b57d652b82bfa5e38bf5dbea0813eed971612671cb9e90c0fe", size = 1975526, upload-time = "2025-10-11T18:40:13.585Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/c2/62/96b5217b742805236614f05904541000f55422a6060a90d7fd4ce26c172d/alembic-1.16.4-py3-none-any.whl", hash = "sha256:b05e51e8e82efc1abd14ba2af6392897e145930c3e0a2faf2b0da2f7f7fd660d", size = 247026, upload-time = "2025-07-10T16:17:21.845Z" },
+    { url = "https://files.pythonhosted.org/packages/44/1f/38e29b06bfed7818ebba1f84904afdc8153ef7b6c7e0d8f3bc6643f5989c/alembic-1.17.0-py3-none-any.whl", hash = "sha256:80523bc437d41b35c5db7e525ad9d908f79de65c27d6a5a5eab6df348a352d99", size = 247449, upload-time = "2025-10-11T18:40:16.288Z" },
 ]
 
 [[package]]
@@ -67,16 +67,16 @@ wheels = [
 
 [[package]]
 name = "anyio"
-version = "4.10.0"
+version = "4.11.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "idna" },
     { name = "sniffio" },
     { name = "typing-extensions", marker = "python_full_version < '3.13'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/f1/b4/636b3b65173d3ce9a38ef5f0522789614e590dab6a8d505340a4efe4c567/anyio-4.10.0.tar.gz", hash = "sha256:3f3fae35c96039744587aa5b8371e7e8e603c0702999535961dd336026973ba6", size = 213252, upload-time = "2025-08-04T08:54:26.451Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/c6/78/7d432127c41b50bccba979505f272c16cbcadcc33645d5fa3a738110ae75/anyio-4.11.0.tar.gz", hash = "sha256:82a8d0b81e318cc5ce71a5f1f8b5c4e63619620b63141ef8c995fa0db95a57c4", size = 219094, upload-time = "2025-09-23T09:19:12.58Z" }
 wheels = [
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@@ -1278,6 +1806,7 @@ source = { editable = "." }
 dependencies = [
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     { name = "cityhash" },
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@@ -1287,158 +1816,180 @@ dependencies = [
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     { name = "tifffile" },
     { name = "torch" },
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diff --git a/visualize_results/convert_images.ipynb b/visualize_results/convert_images.ipynb
deleted file mode 100644
index cf6756b..0000000
--- a/visualize_results/convert_images.ipynb
+++ /dev/null
@@ -1,174 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "jukit_cell_id": "v4hTsDPzNq"
-      },
-      "source": [
-        "# Convert Images\n",
-        "Convert images to a format that can converted by the nViz tiff_to_zarr function."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "jukit_cell_id": "8PUVwERmui"
-      },
-      "source": [
-        "import pathlib\n",
-        "import sys\n",
-        "\n",
-        "import numpy as np\n",
-        "import pandas as pd\n",
-        "import tifffile"
-      ],
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "jukit_cell_id": "ffchTZVcMC"
-      },
-      "source": [
-        "# Inputs\n",
-        "Path to the images"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "jukit_cell_id": "BAK37X1T91"
-      },
-      "source": [
-        "artifacts_path = sys.argv[1]\n",
-        "img_dir_paths = {\n",
-        "    tif_file.parent for tif_file in pathlib.Path(artifacts_path).rglob(\"*.tif*\")\n",
-        "}"
-      ],
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "jukit_cell_id": "pbcRx20UBO"
-      },
-      "source": [
-        "# Outputs"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "jukit_cell_id": "0Ngh7BxR8n"
-      },
-      "source": [
-        "modified_artifacts_path = sys.argv[2]"
-      ],
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "jukit_cell_id": "NrtfvttGAs"
-      },
-      "source": [
-        "def get_base_name(img_path: str) -> str:\n",
-        "    \"\"\"\n",
-        "    Get the image name.\n",
-        "    \"\"\"\n",
-        "\n",
-        "    if \"generated_prediction\" in img_path:\n",
-        "        return \"placeholder_generated-segmentation.tiff\"\n",
-        "    elif \"TRANS\" in img_path:\n",
-        "        return \"placeholder_brightfield.tiff\"\n",
-        "    else:\n",
-        "        return \"placeholder_pipeline-segmentation.tiff\""
-      ],
-      "outputs": [],
-      "execution_count": null
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "jukit_cell_id": "657c1Z5LLx"
-      },
-      "source": [
-        "# Process the Data\n",
-        "- Convert the 3D images to 2D.\n",
-        "- Change the value after the first underscore to the type of image represented so it can be visualized as a layer.\n",
-        "- Create a separate folder for the same fovs with different crop coordinates"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "jukit_cell_id": "HX6LK7i2A8"
-      },
-      "source": [
-        "for img_dir_path in img_dir_paths:\n",
-        "    rel_dir = img_dir_path.relative_to(artifacts_path)\n",
-        "    target_dir_base = modified_artifacts_path / rel_dir\n",
-        "\n",
-        "    for img_path in img_dir_path.rglob(\"*.tif*\"):\n",
-        "        img = tifffile.imread(img_path)\n",
-        "        img_bit_depth = img.dtype.itemsize * 8\n",
-        "\n",
-        "        if img.ndim != 3:\n",
-        "            raise ValueError(\"Script not needed, images should already be 2D\")\n",
-        "\n",
-        "        mod_img_dir = target_dir_base / img_path.stem.split(\"__\")[0]\n",
-        "        mod_img_dir.mkdir(parents=True, exist_ok=True)\n",
-        "\n",
-        "        base_name = get_base_name(str(img_path))\n",
-        "\n",
-        "        for z_index in range(img.shape[0]):\n",
-        "            mod_img_path = (\n",
-        "                mod_img_dir\n",
-        "                / f\"{pathlib.Path(base_name).stem}_{z_index:02d}{pathlib.Path(base_name).suffix}\"\n",
-        "            )\n",
-        "            tifffile.imwrite(mod_img_path, img[z_index])\n",
-        "\n",
-        "            # Generate the thresholded image as well\n",
-        "            if \"generated-segmentation\" in base_name:\n",
-        "                max_pixel_val = (2**img_bit_depth) - 1\n",
-        "                thresh_image = np.where(\n",
-        "                    img > int(max_pixel_val * 0.1), max_pixel_val, 0\n",
-        "                )[z_index]\n",
-        "\n",
-        "                if img_bit_depth == 8:\n",
-        "                    thresh_image = thresh_image.astype(np.uint8)\n",
-        "\n",
-        "                else:\n",
-        "                    thresh_image = thresh_image.astype(np.uint16)\n",
-        "\n",
-        "                tifffile.imwrite(\n",
-        "                    mod_img_dir\n",
-        "                    / f\"{pathlib.Path(base_name).stem}-thresholded_{z_index:02d}{pathlib.Path(base_name).suffix}\",\n",
-        "                    thresh_image,\n",
-        "                )"
-      ],
-      "outputs": [
-        {
-          "output_type": "stream",
-          "name": "stdout",
-          "text": "placeholder_pipeline-segmentation.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_06/NF0016/G4-2/2__2/1024_1280_512_768/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_brightfield.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_brightfield.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_07/NF0016/G4-2/16__16/256_512_768_1024/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_brightfield.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_06/NF0016/G4-2/16__16/256_512_768_1024/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_06/NF0016/F2-1/0__0/768_1024_512_768/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_brightfield.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_brightfield.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_05/NF0016/G4-2/16__16/256_512_768_1024/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_04/NF0016/F7-3/1__1/512_768_1024_1280/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_brightfield.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_04/NF0016/F7-3/1__1/768_1024_1024_1280/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_brightfield.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_brightfield.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_07/NF0016/F7-3/3__3/1024_1280_768_1024/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_05/NF0016/F7-3/1__1/512_768_1024_1280/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_brightfield.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_05/NF0016/F7-3/1__1/768_1024_1024_1280/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_brightfield.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_02/NF0016/G4-2/5__5/768_1024_768_1024/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_brightfield.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_brightfield.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_00/NF0016/G9-1/2__2/1024_1280_512_768/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_brightfield.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_04/NF0016/F7-3/3__3/1024_1280_768_1024/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_00/NF0016/G9-1/16__16/768_1024_768_1024/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_brightfield.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_03/NF0016/G4-2/2__2/1024_1280_512_768/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_brightfield.tiff\nplaceholder_brightfield.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_05/NF0016/F7-3/3__3/1024_1280_768_1024/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_07/NF0016/F7-3/12__12/1024_1280_1024_1280/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_brightfield.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_01/NF0016/G9-1/15__15/768_1024_768_1024/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_brightfield.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_brightfield.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_03/NF0016/G4-2/16__16/256_512_768_1024/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_06/NF0016/F7-3/12__12/1024_1280_1024_1280/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_brightfield.tiff\nplaceholder_brightfield.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_02/NF0016/F2-1/7__7/512_768_768_1024/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_pipeline-segmentation.tiff\npla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ne-segmentation.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_04/NF0016/G4-2/2__2/1024_1280_512_768/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_brightfield.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_01/NF0016/G9-1/16__16/768_1024_768_1024/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_brightfield.tiff\nplaceholder_brightfield.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_02/NF0016/F7-3/3__3/1024_1280_768_1024/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_brightfield.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_02/NF0016/G4-2/16__16/256_512_768_1024/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_brightfield.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_04/NF0016/G4-2/16__16/256_512_768_1024/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_05/NF0016/G4-2/2__2/1024_1280_512_768/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_brightfield.tiff\nplaceholder_brightfield.tiff\nplaceholder_brightfield.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_00/NF0016/F2-1/4__4/768_1024_768_1024/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_00/NF0016/F2-1/4__4/256_512_768_1024/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_brightfield.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_03/NF0016/F2-1/10__10/768_1024_1024_1280/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_05/NF0016/F2-1/0__0/768_1024_512_768/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_brightfield.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_01/NF0016/G4-2/8__8/1024_1280_512_768/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_brightfield.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_04/NF0016/F7-3/12__12/1024_1280_1024_1280/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_brightfield.tiff\nplaceholder_brightfield.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_07/NF0016/F2-1/7__7/512_768_768_1024/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_brightfield.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_04/NF0016/F2-1/7__7/512_768_768_1024/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_00/NF0016/G4-2/2__2/1024_1280_512_768/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_brightfield.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_brightfield.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_00/NF0016/G4-2/16__16/256_512_768_1024/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_07/NF0016/G4-2/8__8/1024_1280_512_768/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_brightfield.tiff\nplaceholder_brightfield.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_07/NF0016/F2-1/10__10/768_1024_1024_1280/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_06/NF0016/G4-2/8__8/1024_1280_512_768/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_brightfield.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_07/NF0016/G9-1/15__15/768_1024_768_1024/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_brightfield.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_00/NF0016/F7-3/12__12/1024_1280_1024_1280/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_brightfield.tiff\nplaceholder_brightfield.tiff\nplaceholder_brightfield.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_01/NF0016/F2-1/4__4/768_1024_768_1024/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_01/NF0016/F2-1/4__4/256_512_768_1024/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_brightfield.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_04/NF0016/F2-1/10__10/768_1024_1024_1280/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_05/NF0016/G4-2/8__8/1024_1280_512_768/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_brightfield.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_06/NF0016/G9-1/15__15/768_1024_768_1024/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_brightfield.tiff\nplaceholder_brightfield.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_00/NF0016/F2-1/7__7/512_768_768_1024/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_05/NF0016/G9-1/15__15/768_1024_768_1024/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_brightfield.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_brightfield.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_01/NF0016/G9-1/2__2/1024_1280_512_768/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_06/NF0016/G4-2/5__5/768_1024_768_1024/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_brightfield.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_02/NF0016/G4-2/2__2/1024_1280_512_768/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_brightfield.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_02/NF0016/F2-1/9__9/256_512_512_768/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_brightfield.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_07/NF0016/F2-1/9__9/256_512_512_768/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_brightfield.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_02/NF0016/F2-1/0__0/768_1024_512_768/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_brightfield.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_03/NF0016/G4-2/8__8/1024_1280_512_768/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_brightfield.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_07/NF0016/F2-1/0__0/768_1024_512_768/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_brightfield.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_05/NF0016/F2-1/9__9/256_512_512_768/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_brightfield.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_03/NF0016/G9-1/15__15/768_1024_768_1024/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_brightfield.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_02/NF0016/F7-3/12__12/1024_1280_1024_1280/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_brightfield.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_03/NF0016/G4-2/5__5/768_1024_768_1024/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_brightfield.tiff\nplaceholder_brightfield.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_00/NF0016/F2-1/10__10/768_1024_1024_1280/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_brightfield.tiff\nplaceholder_brightfield.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_06/NF0016/F2-1/4__4/768_1024_768_1024/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_06/NF0016/F2-1/4__4/256_512_768_1024/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_03/NF0016/F2-1/9__9/256_512_512_768/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_brightfield.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_01/NF0016/F7-3/1__1/512_768_1024_1280/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_brightfield.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_01/NF0016/F7-3/1__1/768_1024_1024_1280/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_brightfield.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_03/NF0016/F2-1/0__0/768_1024_512_768/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_brightfield.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_01/NF0016/G4-2/5__5/768_1024_768_1024/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_brightfield.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_brightfield.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_06/NF0016/G9-1/2__2/1024_1280_512_768/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_07/NF0016/G9-1/16__16/768_1024_768_1024/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_brightfield.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_06/NF0016/G9-1/16__16/768_1024_768_1024/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_brightfield.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_05/NF0016/G9-1/16__16/768_1024_768_1024/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_brightfield.tiff\nplaceholder_brightfield.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_01/NF0016/F2-1/7__7/512_768_768_1024/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_02/NF0016/G4-2/8__8/1024_1280_512_768/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_brightfield.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_04/NF0016/G4-2/8__8/1024_1280_512_768/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_brightfield.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_brightfield.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_03/NF0016/G9-1/2__2/1024_1280_512_768/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_07/NF0016/F7-3/1__1/512_768_1024_1280/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_brightfield.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_07/NF0016/F7-3/1__1/768_1024_1024_1280/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_brightfield.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_02/NF0016/G9-1/15__15/768_1024_768_1024/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_brightfield.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_06/NF0016/F7-3/1__1/512_768_1024_1280/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_brightfield.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_06/NF0016/F7-3/1__1/768_1024_1024_1280/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_brightfield.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_03/NF0016/G9-1/16__16/768_1024_768_1024/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_brightfield.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_04/NF0016/G9-1/15__15/768_1024_768_1024/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_brightfield.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_07/NF0016/G4-2/5__5/768_1024_768_1024/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_brightfield.tiff\nplaceholder_brightfield.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_06/NF0016/F7-3/3__3/1024_1280_768_1024/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_04/NF0016/G4-2/5__5/768_1024_768_1024/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_brightfield.tiff\nplaceholder_brightfield.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_01/NF0016/F2-1/10__10/768_1024_1024_1280/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_05/NF0016/G4-2/5__5/768_1024_768_1024/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_brightfield.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_04/NF0016/F2-1/9__9/256_512_512_768/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_brightfield.tiff\nplaceholder_brightfield.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_06/NF0016/F2-1/7__7/512_768_768_1024/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_04/NF0016/F2-1/0__0/768_1024_512_768/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_brightfield.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_03/NF0016/F7-3/1__1/512_768_1024_1280/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_brightfield.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_03/NF0016/F7-3/1__1/768_1024_1024_1280/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_brightfield.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_00/NF0016/G4-2/8__8/1024_1280_512_768/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_brightfield.tiff\nplaceholder_brightfield.tiff\nplaceholder_brightfield.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_02/NF0016/F2-1/4__4/768_1024_768_1024/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_02/NF0016/F2-1/4__4/256_512_768_1024/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_brightfield.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_03/NF0016/F7-3/3__3/1024_1280_768_1024/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_00/NF0016/G9-1/15__15/768_1024_768_1024/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_brightfield.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_brightfield.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_01/NF0016/G4-2/16__16/256_512_768_1024/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_brightfield.tiff\nplaceholder_brightfield.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_05/NF0016/F2-1/4__4/768_1024_768_1024/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_05/NF0016/F2-1/4__4/256_512_768_1024/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_00/NF0016/G4-2/5__5/768_1024_768_1024/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_brightfield.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_brightfield.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_07/NF0016/G9-1/2__2/1024_1280_512_768/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_brightfield.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_06/NF0016/F2-1/10__10/768_1024_1024_1280/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_brightfield.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_01/NF0016/F7-3/3__3/1024_1280_768_1024/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_brightfield.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_04/NF0016/G9-1/2__2/1024_1280_512_768/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_02/NF0016/G9-1/16__16/768_1024_768_1024/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_brightfield.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_00/NF0016/F2-1/9__9/256_512_512_768/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_brightfield.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_04/NF0016/G9-1/16__16/768_1024_768_1024/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_brightfield.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_brightfield.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_05/NF0016/G9-1/2__2/1024_1280_512_768/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_00/NF0016/F2-1/0__0/768_1024_512_768/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_brightfield.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_01/NF0016/F7-3/12__12/1024_1280_1024_1280/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_brightfield.tiff\nplaceholder_brightfield.tiff\nplaceholder_brightfield.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_03/NF0016/F2-1/4__4/768_1024_768_1024/placeholder_generated-segmentation-thresholded_00.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_pipeline-segmentation.tiff\nplaceholder_generated-segmentation.tiff\nHello\ntemp_dir/artifacts/epoch_03/NF0016/F2-1/4__4/256_512_768_1024/placeholder_generated-segmentation-thresholded_00.tiff\n"
-        }
-      ],
-      "execution_count": 1
-    }
-  ],
-  "metadata": {
-    "anaconda-cloud": {},
-    "kernelspec": {
-      "display_name": "python",
-      "language": "python",
-      "name": "python3"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 4
-}
\ No newline at end of file
diff --git a/visualize_results/nbconverted/convert_images.py b/visualize_results/nbconverted/convert_images.py
deleted file mode 100644
index 2efcaec..0000000
--- a/visualize_results/nbconverted/convert_images.py
+++ /dev/null
@@ -1,103 +0,0 @@
-#!/usr/bin/env python
-# coding: utf-8
-
-# # Convert Images
-# Convert images to a format that can converted by the nViz tiff_to_zarr function.
-
-# In[ ]:
-
-
-import pathlib
-import sys
-
-import numpy as np
-import pandas as pd
-import tifffile
-
-
-# # Inputs
-# Path to the images
-
-# In[ ]:
-
-
-artifacts_path = sys.argv[1]
-img_dir_paths = {
-    tif_file.parent for tif_file in pathlib.Path(artifacts_path).rglob("*.tif*")
-}
-
-
-# # Outputs
-
-# In[ ]:
-
-
-modified_artifacts_path = sys.argv[2]
-
-
-# In[ ]:
-
-
-def get_base_name(img_path: str) -> str:
-    """
-    Get the image name.
-    """
-
-    if "generated_prediction" in img_path:
-        return "placeholder_generated-segmentation.tiff"
-    elif "TRANS" in img_path:
-        return "placeholder_brightfield.tiff"
-    else:
-        return "placeholder_pipeline-segmentation.tiff"
-
-
-# # Process the Data
-# - Convert the 3D images to 2D.
-# - Change the value after the first underscore to the type of image represented so it can be visualized as a layer.
-# - Create a separate folder for the same fovs with different crop coordinates
-
-# In[1]:
-
-
-for img_dir_path in img_dir_paths:
-    rel_dir = img_dir_path.relative_to(artifacts_path)
-    target_dir_base = modified_artifacts_path / rel_dir
-
-    for img_path in img_dir_path.rglob("*.tif*"):
-        img = tifffile.imread(img_path)
-        img_bit_depth = img.dtype.itemsize * 8
-
-        if img.ndim != 3:
-            raise ValueError("Script not needed, images should already be 2D")
-
-        mod_img_dir = target_dir_base / img_path.stem.split("__")[0]
-        mod_img_dir.mkdir(parents=True, exist_ok=True)
-
-        base_name = get_base_name(str(img_path))
-
-        for z_index in range(img.shape[0]):
-            mod_img_path = (
-                mod_img_dir
-                / f"{pathlib.Path(base_name).stem}_{z_index:02d}{pathlib.Path(base_name).suffix}"
-            )
-            tifffile.imwrite(mod_img_path, img[z_index])
-
-            # Generate the thresholded image as well
-            if "generated-segmentation" in base_name:
-                max_pixel_val = (2**img_bit_depth) - 1
-                thresh_image = np.where(
-                    img > int(max_pixel_val * 0.1), max_pixel_val, 0
-                )[z_index]
-
-                if img_bit_depth == 8:
-                    thresh_image = thresh_image.astype(np.uint8)
-
-                else:
-                    thresh_image = thresh_image.astype(np.uint16)
-
-                tifffile.imwrite(
-                    mod_img_dir
-                    / f"{pathlib.Path(base_name).stem}-thresholded_{z_index:02d}{pathlib.Path(base_name).suffix}",
-                    thresh_image,
-                )
-
diff --git a/visualize_results/view_images.sh b/visualize_results/view_images.sh
deleted file mode 100755
index b86c009..0000000
--- a/visualize_results/view_images.sh
+++ /dev/null
@@ -1,28 +0,0 @@
-#!/bin/bash
-# Converts and views images
-
-set -e
-
-# Path to the folder containing the mlflow images
-mlflow_artifacts_path=$1
-
-temp_image_dir=$(mktemp -d)
-
-uv run python convert_images.py "$mlflow_artifacts_path" "$temp_image_dir"
-
-while true; do
-    temp_zarr_dir=$(mktemp -d)
-    chosen_dir=$(find "$temp_image_dir" -type f -name "*.tiff" -exec dirname {} \; | sort -u | fzf -x) || exit
-    echo "You choose the directory $chosen_dir"
-
-    uvx --python 3.13 --with nviz==0.0.4 nviz tiff_to_zarr \
-        --image_dir "$chosen_dir" \
-        --output_path "$temp_zarr_dir/output.zarr" \
-        --channel_map '{"input":"Brightfield","generated-prediction":"Generated Prediction", "segmentation":"Generated Segmentation", "TRANS":"Pipeline Segmentation"}' \
-        --scaling_values '(1.0,0.1,0.1)'  # Microscope values
-
-    uvx --python 3.13 --with nviz==0.0.4 nviz view_zarr \
-        --zarr_dir "$temp_zarr_dir/output.zarr" \
-        --scaling_values '(1.0,0.1,0.1)' \
-        --headless "False"
-done
diff --git a/visualize_results_nviz/convert_view_images.ipynb b/visualize_results_nviz/convert_view_images.ipynb
new file mode 100644
index 0000000..7b3c4b8
--- /dev/null
+++ b/visualize_results_nviz/convert_view_images.ipynb
@@ -0,0 +1,140 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "metadata": {
+        "jukit_cell_id": "SJRJJQg9bl"
+      },
+      "source": [
+        "import pathlib\n",
+        "import sys\n",
+        "import tempfile\n",
+        "\n",
+        "import numpy as np\n",
+        "import tifffile\n",
+        "from nviz.image import tiff_to_zarr\n",
+        "from nviz.view import view_zarr_with_napari"
+      ],
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "jukit_cell_id": "gCB7v5DWXR"
+      },
+      "source": [
+        "# Inputs"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "jukit_cell_id": "WikowAu85E"
+      },
+      "source": [
+        "original_img_dir_path = pathlib.Path(\"temp_example\").resolve(strict=True)\n",
+        "img_dir_paths = {tif_file for tif_file in original_img_dir_path.rglob(\"*.tif*\")}"
+      ],
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "jukit_cell_id": "jdei3LohYa"
+      },
+      "source": [
+        "# Parameters\n",
+        "Came from the microscope"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "jukit_cell_id": "dBrcS9cJKV"
+      },
+      "source": [
+        "scaling_values = [1.0, 0.1, 0.1]"
+      ],
+      "outputs": [],
+      "execution_count": null
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "jukit_cell_id": "fs2zGngOsp"
+      },
+      "source": [
+        "# Split, Convert, and View Images"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "jukit_cell_id": "CS62SbkulZ"
+      },
+      "source": [
+        "with tempfile.TemporaryDirectory() as tmpdir:\n",
+        "    temp_img_dir_path = pathlib.Path(tmpdir) / \"tiff_images\"\n",
+        "    temp_img_dir_path.mkdir(parents=True, exist_ok=True)\n",
+        "\n",
+        "    temp_zarr_dir_path = pathlib.Path(tmpdir) / \"zarr_output\"\n",
+        "    temp_zarr_dir_path.mkdir(parents=True, exist_ok=True)\n",
+        "\n",
+        "    for img_path in img_dir_paths:\n",
+        "\n",
+        "        image = tifffile.imread(img_path)\n",
+        "        image_dir = img_path.relative_to(original_img_dir_path)\n",
+        "\n",
+        "        # Save 2D representations of 3D tiff images\n",
+        "        # nViz can't convert 3D tiff images to an OME-tiff or OME-zarr format yet\n",
+        "        for zslice_idx in range(image.shape[0]):\n",
+        "            save_path = temp_img_dir_path / image_dir\n",
+        "            pathlib.Path(save_path.parent).mkdir(parents=True, exist_ok=True)\n",
+        "            save_path = save_path.with_name(\n",
+        "                f\"{save_path.stem}_{zslice_idx:02d}{save_path.suffix}\"\n",
+        "            )\n",
+        "            tifffile.imwrite(save_path, image[zslice_idx])\n",
+        "            thresholded_segmentations_save_path = save_path.parent\n",
+        "\n",
+        "            segmentation_image_path = (\n",
+        "                thresholded_segmentations_save_path\n",
+        "                / f\"3D_generated-segmentation_{zslice_idx:02d}.tiff\"\n",
+        "            )\n",
+        "\n",
+        "            # Save 2D thresholded segmentations images\n",
+        "            if not segmentation_image_path.exists():\n",
+        "                tifffile.imwrite(\n",
+        "                    segmentation_image_path,\n",
+        "                    (image[zslice_idx] >= 55).astype(np.uint8) * 255,\n",
+        "                )\n",
+        "\n",
+        "    tiff_to_zarr(\n",
+        "        image_dir=save_path.parent,\n",
+        "        output_path=temp_zarr_dir_path / \"output.zarr\",\n",
+        "        channel_map={\"TRANS\": \"brightfield\"},\n",
+        "        scaling_values=scaling_values,\n",
+        "    )\n",
+        "\n",
+        "    viewer = view_zarr_with_napari(\n",
+        "        zarr_dir=temp_zarr_dir_path / \"output.zarr\",\n",
+        "        scaling_values=scaling_values,\n",
+        "        headless=False,\n",
+        "    )"
+      ],
+      "outputs": [],
+      "execution_count": null
+    }
+  ],
+  "metadata": {
+    "anaconda-cloud": {},
+    "kernelspec": {
+      "display_name": "python",
+      "language": "python",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 4
+}
\ No newline at end of file
diff --git a/visualize_results_nviz/nbconverted/convert_view_images.py b/visualize_results_nviz/nbconverted/convert_view_images.py
new file mode 100644
index 0000000..6f5292a
--- /dev/null
+++ b/visualize_results_nviz/nbconverted/convert_view_images.py
@@ -0,0 +1,87 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[ ]:
+
+
+import pathlib
+import sys
+import tempfile
+
+import numpy as np
+import tifffile
+from nviz.image import tiff_to_zarr
+from nviz.view import view_zarr_with_napari
+
+
+# # Inputs
+
+# In[ ]:
+
+
+original_img_dir_path = pathlib.Path("temp_example").resolve(strict=True)
+img_dir_paths = {tif_file for tif_file in original_img_dir_path.rglob("*.tif*")}
+
+
+# # Parameters
+# Came from the microscope
+
+# In[ ]:
+
+
+scaling_values = [1.0, 0.1, 0.1]
+
+
+# # Split, Convert, and View Images
+
+# In[ ]:
+
+
+with tempfile.TemporaryDirectory() as tmpdir:
+    temp_img_dir_path = pathlib.Path(tmpdir) / "tiff_images"
+    temp_img_dir_path.mkdir(parents=True, exist_ok=True)
+
+    temp_zarr_dir_path = pathlib.Path(tmpdir) / "zarr_output"
+    temp_zarr_dir_path.mkdir(parents=True, exist_ok=True)
+
+    for img_path in img_dir_paths:
+
+        image = tifffile.imread(img_path)
+        image_dir = img_path.relative_to(original_img_dir_path)
+
+        # Save 2D representations of 3D tiff images
+        # nViz can't convert 3D tiff images to an OME-tiff or OME-zarr format yet
+        for zslice_idx in range(image.shape[0]):
+            save_path = temp_img_dir_path / image_dir
+            pathlib.Path(save_path.parent).mkdir(parents=True, exist_ok=True)
+            save_path = save_path.with_name(
+                f"{save_path.stem}_{zslice_idx:02d}{save_path.suffix}"
+            )
+            tifffile.imwrite(save_path, image[zslice_idx])
+            thresholded_segmentations_save_path = save_path.parent
+
+            segmentation_image_path = (
+                thresholded_segmentations_save_path
+                / f"3D_generated-segmentation_{zslice_idx:02d}.tiff"
+            )
+
+            # Save 2D thresholded segmentations images
+            if not segmentation_image_path.exists():
+                tifffile.imwrite(
+                    segmentation_image_path,
+                    (image[zslice_idx] >= 55).astype(np.uint8) * 255,
+                )
+
+    tiff_to_zarr(
+        image_dir=save_path.parent,
+        output_path=temp_zarr_dir_path / "output.zarr",
+        channel_map={"TRANS": "brightfield"},
+        scaling_values=scaling_values,
+    )
+
+    viewer = view_zarr_with_napari(
+        zarr_dir=temp_zarr_dir_path / "output.zarr",
+        scaling_values=scaling_values,
+        headless=False,
+    )
+