VerSe`19: Large Scale Vertebrae Segmentation Challenge

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



The order of the talks does not necessarily indicate the ranking. The final ranks will be announced on the day of the challenge.


Overview

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)


Sponsorship

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)