Report on the AAPM grand challenge on deep generative modeling for learning medical image statistics

作者：R. Deshpande, Varun A. Kelkar, Dimitrios S. Gotsis, Prabhat KC, Rongping Zeng, Kyle J. Myers, Frank J. Brooks, Mark A. Anastasio · 发表于：Medical Physics · 年份：2024 · DOI：10.1002/mp.17473 · 被引用次数：9 · 研究领域：Generative Adversarial Networks and Image Synthesis、Cell Image Analysis Techniques、Artificial Intelligence in Healthcare and Education

BACKGROUND: The findings of the 2023 AAPM Grand Challenge on Deep Generative Modeling for Learning Medical Image Statistics are reported in this Special Report. PURPOSE: The goal of this challenge was to promote the development of deep generative models for medical imaging and to emphasize the need for their domain-relevant assessments via the analysis of relevant image statistics. METHODS: 512. For the evaluation of submissions to the Challenge, an ensemble of 10 000 DGM-generated images from each submission was employed. The evaluation procedure consisted of two stages. In the first stage, a preliminary check for memorization and image quality (via the Fréchet Inception Distance [FID]) was performed. Submissions that passed the first stage were then evaluated for the reproducibility of image statistics corresponding to several feature families including texture, morphology, image moments, fractal statistics, and skeleton statistics. A summary measure in this feature space was employed to rank the submissions. Additional analyses of submissions was performed to assess DGM performance specific to individual feature families, the four classes in the training data, and also to identify various artifacts. RESULTS: Fifty-eight submissions from 12 unique users were received for this Challenge. Out of these 12 submissions, 9 submissions passed the first stage of evaluation and were eligible for ranking. The top-ranked submission employed a conditional latent diffusion model, wherea...