VerSe'20 CT Dataset

@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}
}