Multi-view Evidential Learning-based Medical Image Segmentation

Medical image segmentation provides useful information about the shape and size of organs, which is beneficial for improving diagnosis, analysis, and treatment. Despite traditional deep learning-based models can extract domain-specific knowledge, they face a generalization bottleneck due to the limited embedded knowledge scope. Vision foundation models have been demonstrated to be effective in extracting generalizable knowledge, but they cannot extract domain-specific knowledge without fine-tuning. In this work, we propose a novel multi-view evidential learning-based framework, which can extract both domain-specific and generalizable knowledge from multi-view features by combining the advantages of traditional and vision foundation models. Specifically, a novel multi-view state space model (MV-SSM) is designed to extract task-related knowledge while removing redundant information within multi-view features. The proposed MV-SSM utilizes Mamba, a state space model, to model cross-view contextual dependencies between domain-specific and generalizable features. Additionally, evidential learning is adopted to quantify the segmentation uncertainty of the model for boundary. In special, variational Dirichlet is introduced to characterize the distribution of the result probabilities, parameterized with collected evidence to quantify uncertainty. As a result, the model can reduce the segmentation uncertainties of boundaries by optimizing the parameters of the Dirichlet distribution. Experimental results on three datasets show that our method obtains superior segmentation performance.