The models submitted and to be considered for this challenge must be
fully automated. The submissions will be evaluated using the weighted
Sorensen-Dice coefficient across cartilage and bone on a held-out test
set that will be under-sampled by 8x in the equivalent pattern as
training data and generates multi-class segmentation masks as output.
Relevant scripts for computing the evaluation metric and a template
for creating the submission file will be shared.
No additional data allowed for training.
Members associated with UCSF may participate but are not eligible for
awards and not listed in leaderboard.
Participants with the top weighted dice scores for multi-class
segmentation (cartilage, bone) will be announced publicly on a
leaderboard with potential cash prizes to be determined for the top 3
submissions.
The performance of all submissions will be made public on the
leaderboard unless a specific team withdraws from the competition.
The top 3 scorers will be highlighted and will be awarded cash prizes.
Top 5 submissions will be invited to contribute to a full-length
challenge summary research article.
