In the spirit of cultivating and promoting research on machine learning-based automation and data evaluation, the European Society for Hybrid, Molecular and Translational Imaging (ESHI-MT), in collaboration with the Medical Image Computing and Computer Assisted Intervention Society (MICCAI), has created the first autoPET challenge on automated tumor lesion segmentation on whole-body FDG-PET/CT!
Your mission:
Develop a machine learning algorithm for accurate lesion segmentation of whole-body FDG-PET/CT while also avoiding false positives (brain, bladder, etc.). The 7 top-performing submissions will present their methods at MICCAI 2022 and will also be invited to draft and submit a manuscript to a peer-reviewed journal describing the methods, results, and key findings of the challenge.
But wait…there’s more…
The 7 top-performing submissions will be awarded prizes!
🥇 1st place: 6,000 €
🥈 2nd place: 3,000 €
🥉 3rd place: 2,000 €
🎖️ 4th – 7th place: 1,000 €
Submissions officially opened on Tuesday, May 3rd and will close on Wednesday, August 31st! To learn more about the challenge and how to participate, click here!
