(Wednesday, 7/17/2019) 4:30 PM - 6:00 PM
Room: Stars at Night Ballroom 1
Advancement of imaging research requires years of effort from dedicated research groups around the world. Such groups, working independently, typically suffer from limited local resources in terms of patient data and access to the “ground truth� required to properly train and test their algorithms. When these research groups report their methods in the literature, it is difficult for the research community to compare the relative merits of different approaches, since performance can depend on factors such as database composition, image quality, “truth� definition, and performance evaluation metric. Grand challenges allow for a direct comparison of different algorithms designed for a specific task, with all algorithms following the same set of rules, operating on a common set of data, and being evaluated with a uniform performance assessment approach. Comparisons among the methods of participating groups can help identify approaches that are the most promising for a specific task. The AAPM Working Group on Grand Challenges (WGGC) was created to promote the conduct of grand challenges in medical imaging by (1) developing recommendations for hosting computational challenges through AAPM activities designed to assess or improve the use of medical imaging in diagnostic and/or therapeutic applications, (2) vetting proposals from groups that wish to host a challenge in conjunction with the Annual Meeting, (3) facilitating the execution of these challenges, and (4) generating ideas for challenges to advance the field. Two challenges facilitated by the WGGC were conducted in the months leading up to the 2019 AAPM Annual Meeting and provided a unique opportunity for participants to compare their algorithms with those of other groups in a structured, direct way. Overviews of the Computed Tomography Ventilation Imaging Evaluation Challenge (CTVIE) and the Autosegmentation on MRI for Head-and-Neck Radiation Treatment Planning Challenge (RT-MAC) will be presented along with presentations from the top-performing groups from both challenges.
Learning objectives:
1. To understand the role of grand challenges and public image datasets in medical imaging research.
2. To learn about the methods used by the top-performing participants in the Computed Tomography Ventilation Imaging Evaluation Challenge.
3. To learn about the methods used by the top-performing participants in the Autosegmentation on MRI for Head-and-Neck Radiation Treatment Planning Challenge.
Handouts
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