Contour-Aware Multi-Expert Model for Ambiguous Medical Image Segmentation

• Published in: IEEE transactions on medical imaging Vol. 44; no. 8; pp. 3284 - 3298
• Main Authors: Wang, Jiangnan, Zhou, Caixia, Huang, Yaping
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.08.2025
• Subjects: Accuracy; Algorithms; Ambiguous medical images; Annotations; Codes; contour-based segmentation; Diffusion models; Humans; Image Processing, Computer-Assisted - methods; Image segmentation; Medical diagnostic imaging; Predictive models; Probabilistic logic; Stochastic processes; Uncertainty; uncertainty estimation
• ISSN: 0278-0062; 1558-254X; 1558-254X
• DOI: 10.1109/TMI.2025.3561117

Medical image segmentation is highly challenging due to the uncertainties caused by the inherent ambiguous regions and expert knowledge variations. Some recent works explore the uncertainties and produce multiple outputs to obtain more robust results. However, the quality of the boundary areas remains unsatisfactory. Unfortunately, the key differences among experts usually lie in these boundary areas, which are more critical in practical diagnosis. To tackle the above issues, different from previous pixel-wise segmentation approaches, we present a new perspective and formulate the task as a contour-based regression problem, and further propose a novel Contour-aware Multi-expert Segmentor, named ContourMS, which can provide diverse segmentation results with rich boundary details in a coarse-to-fine manner. Specifically, in the coarse stage, we use a SegmentNet to predict a region mask by leveraging the knowledge of multiple experts, and then the mask is converted to an initial contour shared by all experts. In the fine stage, we design a LatentNet to learn the expert-level latent space and a ContourNet to refine each expert contour, where the deformation guided by the expert style can gradually adjust the contour to match different annotations. Extensive experiments demonstrate that the proposed method can generate diverse segment variants and achieve competitive performance on multiple public multi-expert medical segmentation datasets.