VerSe`19: Large Scale Vertebrae Segmentation Challenge

The Large Scale Vertebrae Segmentation Challenge (VerSe2019) was held in conjunction with MICCAI 2019, Shenzen, China.  

VerSe is now fully open-sourced!


VerSe is happening at MICCAI 2020 too! Jump to VerSe'20

Along the lines of VerSe'20, Incomplete and suspicious registrations will be rejected.

Please make sure to provide appropriate academic or industrial affiliations. If you are an independent researcher,  please mention so in your grand-challenge (GC) profile. 


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.

Key highlights of this challenge include:
  • Public release of a spine dataset with an unprecedented 150 (+50 hidden) 120 (+40 hidden) CT scans with voxel-level vertebral annotations.
  • Two sub-tasks, evaluated and ranked separately: (1) Vertebral localisation and identification. (2) Vertebral segmentation.
  • Submissions can be fully-automated and semi-automated/interactive algorithms.
  • Best-performing in each sub-tasks and of all varieties, along with off-beat approaches will be collected, analysed, and presented in a journal article co-authored with the contributors.
  • Contributors retain the intellectual rights to the submitted codes. The organisers will use it solely towards analysis and manuscripts related to the challenge.

Important Dates:

  • Training data release:
    Phase 1 (16 May, 2019)
    Phase 2 (11 June, 2019)
    Phase 3 (17 July, 2019)
  • Test data release: 01 07 Aug, 2019
  • Submission opens (Test Phase 1 ): 01 07 Aug, 2019
  • Submission deadline (Test Phase 1): 15  21 Extended till 23 Aug, 2019 (11.59PM EDT) 
  • Docker Submission Opens (Test Phase 2): 01  07 Aug, 2019 
  • Docker Submission Closes (Test Phase 2): 30 06 Sep, 2019 (11.59PM EDT)


This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No  637164 — iBack — ERC-2014-STG)