Name | DL | Torrents | Total Size | Medical [edit] | 87 | 2.20TB | 731 | 0 | Computed Tomography [edit] | 6 | 409.30GB | 49 | 0 |
VerSe-complete (3 files)
dataset-verse20validation.zip | 13.15GB |
dataset-verse20training.zip | 11.51GB |
dataset-verse20test.zip | 14.02GB |
Type: Dataset
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Bibtex:
Tags:
Bibtex:
@article{, title= {VerSe'20 CT Dataset}, keywords= {}, author= {}, abstract= {VerSe: A Vertebrae labelling and segmentation benchmark for multi-detector CT images ## What is VerSe? Spine or vertebral segmentation is a crucial step in all applications regarding automated quantification of spinal morphology and pathology. With the advent of deep learning, for such a task on computed tomography (CT) scans, a big and varied data is a primary sought-after resource. However, a large-scale, public dataset is currently unavailable. We believe *VerSe* can help here. VerSe is a large scale, multi-detector, multi-site, CT spine dataset consisting of 374 scans from 355 patients. The challenge was held in two iterations in conjunction with MICCAI 2019 and 2020. The tasks evaluated for include: vertebral labelling and segmentation. ## Citing VerSe If you use VerSe, we would appreciate references to the following papers. 1. **Sekuboyina A et al., VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images, 2021.**<br />In Medical Image Analysis: https://doi.org/10.1016/j.media.2021.102166<br />Pre-print: https://arxiv.org/abs/2001.09193 2. **Löffler M et al., A Vertebral Segmentation Dataset with Fracture Grading. Radiology: Artificial Intelligence, 2020.**<br />In Radiology AI: https://doi.org/10.1148/ryai.2020190138 3. **Liebl H and Schinz D et al., A Computed Tomography Vertebral Segmentation Dataset with Anatomical Variations and Multi-Vendor Scanner Data, 2021.**<br />Pre-print: https://arxiv.org/pdf/2103.06360.pdf ## Data * The dataset has four files corresponding to one data sample: image, segmentation mask, centroid annotations, a PNG overview of the annotations. * Data structure - 01_training - Train data - 02_validation - (Formerly) PUBLIC test data - 03_test - (Formerly) HIDDEN test data * Sub-directory-based arrangement for each patient. File names are constructed of entities, a suffix and a file extension following the conventions of the Brain Imaging Data Structure (BIDS; https://bids.neuroimaging.io/) ``` Example: ------- training/rawdata/sub-verse000 sub-verse000_dir-orient_ct.nii.gz - CT image series training/derivatives/sub-verse000/ sub-verse000_dir-orient_seg-vert_msk.nii.gz - Segmentation mask of the vertebrae sub-verse000_dir-orient_seg-subreg_ctd.json - Centroid coordinates in image space sub-verse000_dir-orient_seg-vert_snp.png - Preview reformations of the annotated CT data. ``` * Centroid coordinates of the subject based structure (.json file) are given in voxels in the image space. 'label' corresponds to the vertebral label: - 1-7: cervical spine: C1-C7 - 8-19: thoracic spine: T1-T12 - 20-25: lumbar spine: L1-L6 - 26: sacrum - not labeled in this dataset - 27: cocygis - not labeled in this dataset - 28: additional 13th thoracic vertebra, T13}, terms= {}, license= {}, superseded= {}, url= {https://github.com/anjany/verse} }