Look well to the spine for the cause of disease - Hippocrates
VerSe examples. Observe the variability in data: field-of-view, fractures, transitional vertebrae, etc.
* [What is VerSe?](#what-is-verse) * [Citing VerSe](#citing-verse) * [Data](#data) * [Download](#download) * [License](#license) * [Code](#code) * [Contact](#contact) * [Other Related Work](#other-related-work) * [Acknowledgements](#acknowledgements)
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.
If you use VerSe, we would appreciate references to the following papers.
Sekuboyina A et al., VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images, 2021.
In Medical Image Analysis: https://doi.org/10.1016/j.media.2021.102166
Pre-print: https://arxiv.org/abs/2001.09193
Löffler M et al., A Vertebral Segmentation Dataset with Fracture Grading. Radiology: Artificial Intelligence, 2020.
In Radiology AI: https://doi.org/10.1148/ryai.2020190138
Liebl H and Schinz D et al., A Computed Tomography Vertebral Segmentation Dataset with Anatomical Variations and Multi-Vendor Scanner Data, 2021.
Pre-print: https://arxiv.org/pdf/2103.06360.pdf
The dataset has four files corresponding to one data sample: image, segmentation mask, centroid annotations, a PNG overview of the annotations.
Data structure
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.
(VerSe'19) https://s3.bonescreen.de/public/VerSe-complete/dataset-verse19test.zip
(VerSe'20) https://s3.bonescreen.de/public/VerSe-complete/dataset-verse20training.zip
Note: The annotation format of the complete VerSe data is NOT identical to the one used for the MICCAI challenges. The OSF repositories above also point to the MICCAI version of the data and annotations. Nonetheless, we recommend usage of the restructured data and annotations
The data is provided under the CC BY-SA 4.0 License, making it fully open-sourced.
The rest of this repository is under the MIT License.
We provide helper code and guiding notebooks.
For queries and issues not fit for a github issue, please email Anjany Sekuboyina or Jan Kirschke .
VerSe has resulted in numerous other publications. Below are a few selected ones.
Sekuboyina A. et al., Labelling Vertebrae with 2D Reformations of Multidetector CT Images: An Adversarial Approach for Incorporating Prior Knowledge of Spine Anatomy. Radiology: Artificial Intelligence, 2020. (https://doi.org/10.1148/ryai.2020190074)