Towards boundary confusion for volumetric medical image segmentation

Accurate boundary segmentation of volumetric images is a critical task for image-guided diagnosis and computer-assisted intervention. It is challenging to address the boundary confusion with explicit constraints. Existing methods of refining boundaries overemphasize the slender structure while overlooking the dynamic interactions between boundaries and neighboring regions. In this paper, we reconceptualize the mechanism of boundary generation via introducing Pushing and Pulling interactions, then propose a unified network termed PP-Net to model shape characteristics of the confused boundary region. Specifically, we first propose the semantic difference module (SDM) from the pushing branch to drive the boundary towards the ground truth under diffusion guidance. Additionally, the class clustering module (CCM) from the pulling branch is introduced to stretch the intersected boundary along the opposite direction. Thus, pushing and pulling branches will furnish two adversarial forces to enhance representation capabilities for the faint boundary. Experiments are conducted on four public datasets and one in-house dataset plagued by boundary confusion. The results demonstrate the superiority of PP-Net over other segmentation networks, especially on the evaluation metrics of Hausdorff Distance and Average Symmetric Surface Distance. Besides, SDM and CCM can serve as plug-and-play modules to enhance classic U-shape baseline models, including recent SAM-based foundation models. Source codes are available at https://github.com/EndoluminalSurgicalVision-IMR/PnPNet.