Annotator Consensus Prediction for Medical Image Segmentation with Diffusion Models
This addresses variability in medical image annotations for clinicians and researchers, but appears incremental as it applies diffusion models to a known bottleneck.
The paper tackles the problem of inter- and intra-observer variability in medical image segmentation by proposing a diffusion model-based method to predict consensus from multiple expert annotations, demonstrating effectiveness and robustness compared to state-of-the-art methods on several datasets.
A major challenge in the segmentation of medical images is the large inter- and intra-observer variability in annotations provided by multiple experts. To address this challenge, we propose a novel method for multi-expert prediction using diffusion models. Our method leverages the diffusion-based approach to incorporate information from multiple annotations and fuse it into a unified segmentation map that reflects the consensus of multiple experts. We evaluate the performance of our method on several datasets of medical segmentation annotated by multiple experts and compare it with state-of-the-art methods. Our results demonstrate the effectiveness and robustness of the proposed method. Our code is publicly available at https://github.com/tomeramit/Annotator-Consensus-Prediction.