PET segmentation challenge

PET segmentation challenge

Automated segmentation of PET images for the delineation of tumor volumes has been the focus of intense research efforts for the last few years [1]. There has also been a few limited efforts to compare several methods on common datasets [2], but in the majority of cases, each method has been evaluated on different image sets according to different evaluation criteria, and a comparison of currently available methods based on literature analysis only is thus challenging, if not impossible. A MICCAI challenge is an interesting opportunity to compare numerous existing methods implemented by their original authors (thus avoiding issues associated with re-implementation of methods by others [3]) on a common set of images. This challenge will be organized and funded by France Life Imaging, a national French infrastructure dedicated to medical imaging sciences, and co-sponsored by the taskgroup 211 “Classification, Advantages and Limitations of the Auto-Segmentation Approaches for PET” of the American Association of Physicists in Medicine (AAPM)[1], following in particular the recommendations it has set regarding benchmarking efforts (choice of image datasets and evaluation strategies) [4], [5].