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CTSpine1K

Dataset Information

The translation of the text is as follows:

CTSpine1K is a large-scale CT dataset specifically designed for spinal segmentation. This dataset comprises a total of 1005 CT cases from four public datasets, with annotations for 25 types of vertebrae, including C1-C7, T1-T12, and L1-L6. It should be noted that most patients do not have an L6 vertebra, hence this category of data is relatively rare. The four public datasets are: COLONOG, HNSCC-3DCT-RT, MSD Liver, and COVID-19. CTSpine1K did not adopt all the data from these datasets; instead, it selected and excluded parts with poor quality. To achieve a reasonable data partition, CTSpine1K ensures that the proportion of data from the four datasets is the same in the training, validation, and test sets. Specifically, the division is 610 cases for the training set, 197 cases for the validation set, and 198 cases for the test set. The specific data composition is as follows:

CTSpine1K consists of four public data sets in the middle.

Dataset Meta Information

Dimensions Modality Task Type Anatomical Structures Anatomical Area Number of Categories Data Volume File Format
3D CT Segmentation cervical vertebra, thoracic, lumbar spine Vertebrae 25 610 for training, 197 for validation, 198 for test. .nii.gz

Number of slices of all data: 506,634

Resolution Details

Dataset Statistics spacing (mm) size
min (0.45, 0.45, 0.45) (512, 512, 42)
median (0.76, 0.76, 0.80) (512, 512, 535)
max (1.27, 1.27, 5.0 ) (512, 512, 1026)

Label Information Statistics

Label Anatomy Structure Detection Count Detection Rate Min Volume (cm³) Median Volume (cm³)
1 C1 (Primary Vertebra) 33 3.28% 19.15 13.94
2 C2 (Secondary Vertebra) 31 3.08% 26.5 17.48
3 C3 (Tertiary Vertebra) 31 3.08% 19.44 12.42
4 C4 (Intervertebral) 35 3.48% 19.48 12.08
5 C5 (Arch Root) 50 4.98% 21.92 10.66
6 C6 (Small Joint) 105 10.45% 22.95 5.38
7 C7 (Upper Joint) 134 13.33% 25.42 14.18
8 T1 (First Lumbar) 136 13.53% 31.88 20.66
9 T2 (Second Lumbar) 136 13.53% 33.91 23.06
10 T3 (Third Lumbar) 137 13.63% 33.47 22.1
11 T4 (Fourth Lumbar) 146 14.53% 33.59 21.4
12 T5 (Fifth Lumbar) 167 16.62% 36.57 21.73
13 T6 (Sixth Lumbar) 291 28.96% 40.33 17.85
14 T7 (Seventh Lumbar) 489 48.66% 44 3.7
15 T8 (Eighth Lumbar) 701 69.75% 53.38 13.23
16 T9 (Ninth Lumbar) 859 85.47% 55.72 26.45
17 T10 (Tenth Lumbar) 938 93.33% 67.62 34.48
18 T11 (Eleventh Lumbar) 970 96.52% 69.98 39.53
19 T12 (Twelfth Lumbar) 972 96.72% 88.12 44.68
20 L1 (First Sacral) 967 96.22% 95.22 50.51
21 L2 (Second Sacral) 953 94.83% 105.88 55.55
22 L3 (Third Sacral) 939 93.43% 120.27 61.62
23 L4 (Fourth Sacral) 932 92.74% 122.35 62.14
24 L5 (Fifth Sacral) 921 91.64% 129.28 62.3
25 L6 (Sixth Sacral) 18 1.79% 92.45 62.72

Visualization

Official website visualization.

File Structure

The official file structure is as follows: the data directory contains CT images from four public datasets. At the same time, the label directory stores the corresponding annotation information and additionally provides complete annotations for the VerSe dataset, which is not part of this dataset. The officials also include the data_split.txt data partition file and the pretrained nnU-Net model model_final_checkpoint.

CTSpine1K
|-- Path.csv
|-- data_split.txt
|-- model_final_checkpoint.model
|-- model_final_checkpoint.model.pkl
|-- plans.pkl
|-- readme.txt
|-- data
|   |-- COVID-19
|   |   |-- volume-covid19-A-0003_ct.nii.gz
|   |   |-- ...
|   |-- HNSCC-3DCT-RT_neck
|   |   |-- HN_P001.nii.gz
|   |   |-- ...
|   |-- colon
|   |   |-- 1.3.6.1.4.1.9328.50.4.0001.nii.gz
|   |   |-- ...
|   |-- liver
|   |   |-- liver_0.nii.gz
|   |   |-- ...
|   |-- metadata_colonog.xlsx
|   |-- metadata_neck.xlsx
|-- label
|   |-- COVID-19
|   |-- HNSCC-3DCT-RT_neck
|   |-- Liver
|   |-- Verse
|   |-- completed_annotation_verse
|   |-- conlon

Authors and Institutions

Yang Deng (Institute of Computing Technology, Chinese Academy of Sciences; Suzhou Institute for Advanced Study, University of Science and Technology of China)

Ce Wang (Institute of Computing Technology, Chinese Academy of Sciences; Suzhou Institute for Advanced Study, University of Science and Technology of China)

Yuan Hui (Institute of Computing Technology, Chinese Academy of Sciences; Suzhou Institute for Advanced Study, University of Science and Technology of China)

Qian Li (Institute of Computing Technology, Chinese Academy of Sciences; Suzhou Institute for Advanced Study, University of Science and Technology of China)

Jun Li (Institute of Computing Technology, Chinese Academy of Sciences)

Shiwei Luo (Department of Radiology, Guangzhou First People's Hospital; School of Medicine, Southern University of Science and Technology)

Mengke Sun (Institute of Computing Technology, Chinese Academy of Sciences)

Quan Quan (Institute of Computing Technology, Chinese Academy of Sciences)

Shuxin Yang (Institute of Computing Technology, Chinese Academy of Sciences)

You Hao (Institute of Computing Technology, Chinese Academy of Sciences; Suzhou Institute for Advanced Study, University of Science and Technology of China)

Pengbo Liu (Institute of Computing Technology, Chinese Academy of Sciences)

Honghu Xiao (Beijing Jishuitan Hospital)

Chunpeng Zhao (Beijing Jishuitan Hospital)

Xinbao Wu (Beijing Jishuitan Hospital)

S. Kevin Zhou (School of Biomedical Engineering and Suzhou Institute for Advanced Research MIRACLE Lab, University of Science and Technology of China; Institute of Computing Technology, Chinese Academy of Sciences)

Source Information

Official Website: https://github.com/MIRACLE-Center/CTSpine1K

Download Link: https://github.com/MIRACLE-Center/CTSpine1K

Article Address: https://arxiv.org/abs/2105.14711

Publication Date: 2021-05

Citation

@misc{deng2021ctspine1k,
      title={CTSpine1K: A Large-Scale Dataset for Spinal Vertebrae Segmentation in Computed Tomography}, 
      author={Yang Deng and Ce Wang and Yuan Hui and Qian Li and Jun Li and Shiwei Luo and Mengke Sun and Quan Quan and Shuxin Yang and You Hao and Pengbo Liu and Honghu Xiao and Chunpeng Zhao and Xinbao Wu and S. Kevin Zhou},
      year={2021},
      eprint={2105.14711},
      archivePrefix={arXiv},
      primaryClass={eess.IV}
}

Original introduction article is here.