Merge pull request #1155 from sarthakpati/master

Version update and documentation updates and some minor fixes

Author: AlexanderGetka-cbica

2_GettingStarted.txt

@@ -70,9 +70,9 @@ In addition, CaPTk offers the ability to extract and visualize commonly used mea
 
 References:
 
--# J.M.Soares, P.Marques, V.Alves, N.Sousa. "A hitchhiker's guide to diffusion tensor imaging", Front Neurosci. 7:31, 2013. DOI: 10.33
+-# J.M.Soares, P.Marques, V.Alves, N.Sousa. "A hitchhiker's guide to diffusion tensor imaging", Front Neurosci. 7:31, 2013, DOI: 10.33
 fnins.2013.00031
--# E.S.Paulson, K.M.Schmainda, "Comparison of dynamic susceptibility-weighted contrast-enhanced MR methods: recommendations for measuring relative cerebral blood volume in brain tumors", Radiology. 249(2):601-613, 2008. DOI: 10.1148/radiol.2492071659
+-# E.S.Paulson, K.M.Schmainda, "Comparison of dynamic susceptibility-weighted contrast-enhanced MR methods: recommendations for measuring relative cerebral blood volume in brain tumors", Radiology. 249(2):601-613, 2008, DOI: 10.1148/radiol.2492071659
 
 --------
 
@@ -182,13 +182,13 @@ Application-specific tissue types are automatically enabled when the correspondi
 
 References:
 
--# A.Gooya, K.M.Pohl, M.Billelo, G.Biros, C. Davatzikos, "Joint segmentation and deformable registration of brain scans guided by a tumog rowth model", Med Image Comput Comput Assist Interv. 14(Pt 2):532-40, 2011. DOI:10.1007/978-3-642-23629-7_65
--# A.Gooya, K.M.Pohl, M.Bilello, L.Cirillo, G.Biros, E.R.Melhem, C.Davatzikos, "GLISTR: glioma image segmentation and registration", IEET rans Med Imaging. 31(10):1941-54, 2012. DOI:10.1109/TMI.2012.2210558
--# D.Kwon, R.T.Shinohara, H.Akbari, C.Davatzikos, "Combining Generative Models for Multifocal Glioma Segmentation and Registration", MeI mage Comput Comput Assist Interv. 17(Pt 1):763-70, 2014. DOI:10.1007/978-3-319-10404-1_95
+-# A.Gooya, K.M.Pohl, M.Billelo, G.Biros, C. Davatzikos, "Joint segmentation and deformable registration of brain scans guided by a tumog rowth model", Med Image Comput Comput Assist Interv. 14(Pt 2):532-40, 2011, DOI:10.1007/978-3-642-23629-7_65
+-# A.Gooya, K.M.Pohl, M.Bilello, L.Cirillo, G.Biros, E.R.Melhem, C.Davatzikos, "GLISTR: glioma image segmentation and registration", IEET rans Med Imaging. 31(10):1941-54, 2012, DOI:10.1109/TMI.2012.2210558
+-# D.Kwon, R.T.Shinohara, H.Akbari, C.Davatzikos, "Combining Generative Models for Multifocal Glioma Segmentation and Registration", MeI mage Comput Comput Assist Interv. 17(Pt 1):763-70, 2014, DOI:10.1007/978-3-319-10404-1_95
 -# S.Bakas, K.Zeng, A.Sotiras, S.Rathore, H.Akbari, B.Gaonkar, M.Rozycki, S.Pati, C.Davatzikos, "Segmentation of gliomas in multimodam agnetic resonance imaging volumes based on a hybrid generative-discriminative framework", In Proc. Multimodal Brain Tumor Segmentation (BraTS) Challenge. 4:5-12, 2015.
--# S.Bakas, K.Zeng, A.Sotiras, S.Rathore, H.Akbari, B.Gaonkar, M.Rozycki, S.Pati, C.Davatzikos, "GLISTRboost: combining multimodal MRs egmentation, registration, and biophysical tumor growth modeling with gradient boosting machines for glioma segmentation", Brainlesion (2015). 9556:144-155, 2016. DOI:10.1007/978-3-319-30858-6_1
--# S.Bakas, H.Akbari, A.Sotiras, M.Bilello, M.Rozycki, J.Kirby, J.Freymann, K.Farahani, C.Davatzikos, "Advancing The Cancer Genome Atlag lioma MRI collections with expert segmentation labels and radiomic features", Nature Scientific Data 4:170117, 2017. DOI:10.103s data.2017.117
--# D.Kwon, M.Niethammer, H.Akbari, M.Bilello, C.Davatzikos, K.M.Pohl, "PORTR: Pre-Operative and Post-Recurrence Brain Tumor Registration", IEEE Trans Med Imaging. 33(3):651-667, 2014. DOI:10.1109/TMI.2013.2293478
+-# S.Bakas, K.Zeng, A.Sotiras, S.Rathore, H.Akbari, B.Gaonkar, M.Rozycki, S.Pati, C.Davatzikos, "GLISTRboost: combining multimodal MRs egmentation, registration, and biophysical tumor growth modeling with gradient boosting machines for glioma segmentation", Brainlesion (2015). 9556:144-155, 2016, DOI:10.1007/978-3-319-30858-6_1
+-# S.Bakas, H.Akbari, A.Sotiras, M.Bilello, M.Rozycki, J.Kirby, J.Freymann, K.Farahani, C.Davatzikos, "Advancing The Cancer Genome Atlag lioma MRI collections with expert segmentation labels and radiomic features", Nature Scientific Data 4:170117, 2017, DOI:10.103s data.2017.117
+-# D.Kwon, M.Niethammer, H.Akbari, M.Bilello, C.Davatzikos, K.M.Pohl, "PORTR: Pre-Operative and Post-Recurrence Brain Tumor Registration", IEEE Trans Med Imaging. 33(3):651-667, 2014, DOI:10.1109/TMI.2013.2293478
 
 --------
 
@@ -245,33 +245,48 @@ These are the keyboard shortcuts available on CaPTk:
 
 \section gs_utilities Utilities (Command-line only)
 
-To make pipeline construction using CaPTk easier, a bunch of utilities have been provided. They include 
+To make pipeline construction using CaPTk easier, a bunch of utilities have been provided:
+
 -# Resizing
--# DICOM conversion
--# Sanity checking
--# Image header information
+-# Resampling
+-# Image information
 -# Unique values in image
--# Changing pixel values. 
-
-For full details, run the command:
-
-\verbatim
-Utilities.exe -u
-\endverbatim
+-# Get smallest bounding box in mask (optional isotropic bounding box)
+-# Test 2 images
+-# Create mask from threshold
+-# DICOM to NIfTI conversion
+-# NIfTI to DICOM & DICOM-Seg conversion
+-# Re-orient image
+-# Cast image to another pixel type
+-# Thresholding:
+  - Below
+  - Above
+  - Above & Below
+  - Otsu 
+  - Binary
+-# Convert file formats
+-# Extract Image Series from Joined stack and vice-versa (4D <-> 3D)
+-# Transform coordinates from world to image and vice-versa
+-# Label similarity Metrics between 2 label images: pure mathematical formulations are given as output
+-# BraTS similarity Metrics between 2 label images: special considerations for metrics are done in this mode because output needs to be BraTS-compliant.
+-# Collect information from all images in a directory and put it in CSV
+
+For a full list of this functionality and more details, please see the corresponding [How-To page](ht_utilities.html).
 
 --------
 
 \section gs_preprocessing Pre-processing
 Image pre-processing is essential to quantitative image analysis. CaPTk pre-processing tools available under the "Preprocessing" menu are fully-parameterizable and comprise:
 
-   -<b>Denoising.</b> Intensity noise reduction in regions of uniform intensity profile is offered through a low-level image processing method, namely Smallest Univalue Segment Assimilating Nucleus (SUSAN) [1]. This is a custom implementation and does <b>NOT</b> call out to the original implementation distributed by FSL.
-   -<b>Co-registration.</b> Registration of various images to the same anatomical template, for examining anatomically aligned imaging signals in tandem and at the voxel level, is done using the Greedy Registration algorithm [5].
-   -<b>Bias correction.</b> Correction for magnetic field inhomogeneity is provided using a non-parametric non-uniform intensity normalization [2].
-   -<b>Intensity normalization.</b> Conversion of signals across modalities to comparable quantities using histogram matching [4].
-   -<b>Z-Scoring normalization.</b> Images are normalized using a z-scoring mechanism with option to do the normalization within the region of interest or across the entire image. In addition, there is an option to remove outliers & noise from the image by removing a certain percentage of the top and bottom intensity ranges [6].
-   -<b>Histogram Matching</b>
-   -<b>Skull Stripping (Deep Learning based)</b>
-   -<b>Mammogram Pre-processing</b>
+   - <b>Denoising.</b> Intensity noise reduction in regions of uniform intensity profile is offered through a low-level image processing method, namely Smallest Univalue Segment Assimilating Nucleus (SUSAN) [1]. This is a custom implementation and does <b>NOT</b> call out to the original implementation distributed by FSL.
+   - <b>Co-registration.</b> Registration of various images to the same anatomical template, for examining anatomically aligned imaging signals in tandem and at the voxel level, is done using the Greedy Registration algorithm [5].
+   - <b>Bias correction.</b> Correction for magnetic field inhomogeneity is provided using a non-parametric non-uniform intensity normalization [2].
+   - <b>Intensity normalization.</b> Conversion of signals across modalities to comparable quantities using histogram matching [4].
+   - <b>Z-Scoring normalization.</b> Images are normalized using a z-scoring mechanism with option to do the normalization within the region of interest or across the entire image. In addition, there is an option to remove outliers & noise from the image by removing a certain percentage of the top and bottom intensity ranges [6].
+   - <b>Histogram Matching</b>
+   - <b>Skull Stripping (Deep Learning based)</b>
+   - <b>Mammogram Pre-processing</b>
+   - <b>BraTS Pre-processing Pipeline</b>
 
 
 <B>NOTE:</B> An extended set of algorithms are available via the command line utility <b>Preprocessing</b>. For full details, run the command:
@@ -289,12 +304,13 @@ Preprocessing.exe -u
 
 References:
 
--# S.M.Smith, J.M.Brady, "SUSAN - a new approach to low level image processing", Int. J. Comput. Vis. 23(1):45-78, 1997. DOI:10.102A :1007963824710
--# N.J.Tustison, B.B.Avants, P.A.Cook, Y.Zheng, A.Egan, P.A.Yushkevich, J.C.Gee, "N4ITK: Improved N3 Bias Correction", IEEE Trans MeI maging. 29(6):1310-20, 2010. doi: 10.1109/TMI.2010.2046908
--# S.Bauer, L.P.Nolte, M.Reyes, "Skull-stripping for Tumor-bearing Brain Images", arXiv. abs/1204.0357, 2012.
--# L.G.Nyul, J.K.Udupa, X.Zhang, "New Variants of a Method of MRI Scale Standardization", IEEE Trans Med Imaging. 19(2):143-50, 2000D OI:10.1109/42.836373
--# P.A.Yushkevich, J.Pluta, H.Wang, L.E.Wisse, S.Das, D.Wolk, "Fast Automatic Segmentation of Hippocampal Subfields and Medical Temporal Lobe Subregions in 3 Tesla and 7 Tesla MRI, Alzheimer's & Dementia: The Journal of Alzheimer's Association, 12(7), P126-127 
--# T.Rohlfing, N.M.Zahr, E.V.Sullivan, A.Pfefferbaum, "The SRI24 multichannel atlas of normal adult human brain structure", Human Brain Mapping, 31(5):798-819, 2010. DOI:10.1002/hbm.20906 
+-# S.M.Smith, J.M.Brady, "SUSAN - a new approach to low level image processing", Int. J. Comput. Vis. 23(1):45-78, 1997, DOI:10.102A :1007963824710
+-# N.J.Tustison, B.B.Avants, P.A.Cook, Y.Zheng, A.Egan, P.A.Yushkevich, J.C.Gee, "N4ITK: Improved N3 Bias Correction", IEEE Trans MeI maging. 29(6):1310-20, 2010, DOI: 10.1109/TMI.2010.2046908
+-# S.P.Thakur, J.Doshi, S.Pati, S.M.Ha, C.Sako, S.Talbar, U.Kulkarni, C.Davatzikos, G.Erus, S.Bakas, "Brain Extraction on MRI Scans in Presence of Diffuse Glioma:
+Multi-institutional Performance Evaluation of Deep Learning Methods and Robust Modality-Agnostic Training", NeuroImage 2020, DOI:10.1016/j.neuroimage.2020.117081
+-# L.G.Nyul, J.K.Udupa, X.Zhang, "New Variants of a Method of MRI Scale Standardization", IEEE Trans Med Imaging. 19(2):143-50, 2000, DOI:10.1109/42.836373
+-# P.A.Yushkevich, J.Pluta, H.Wang, L.E.Wisse, S.Das, D.Wolk, "Fast Automatic Segmentation of Hippocampal Subfields and Medical Temporal Lobe Subregions in 3 Tesla and 7 Tesla MRI", Alzheimer's & Dementia: The Journal of Alzheimer's Association, 12(7), P126-127, DOI:10.1016/j.jalz.2016.06.205
+-# T.Rohlfing, N.M.Zahr, E.V.Sullivan, A.Pfefferbaum, "The SRI24 multichannel atlas of normal adult human brain structure", Human Brain Mapping, 31(5):798-819, 2010, DOI:10.1002/hbm.20906 
 
 --------
 
@@ -372,6 +388,7 @@ Detailed explanation of using the command line is available in the \ref How_To_G
 |     Windows    |                        7, 8, 10                        |        XP, Vista       |
 |      Linux     | Ubuntu 16.04, 18.04; Debian 9, CentOS 7 (source build) | Ubuntu 14.04; CentOS 6 |
 |      macOS     |                      10.13, 10.14                      |          10.12         |
+|     Docker     |                          N.A.                          |           N.A.         |
 
 \section gs_FAQ_2 What DICOM images are currently supported by CaPTk?