Dynamic Strip Convolution and Adaptive Morphology Perception Plugin for Medical Anatomy Segmentation

Medical anatomy segmentation is essential for computer-aided diagnosis and lesion localization in medical images. For example, segmenting individual ribs benefits localizing the lung lesions and providing vital medical measurements (such as rib spacing) for generating medical reports. Existing methods segment shape-different anatomies (such as striped ribs, bulky lungs, and angular scapula) with the same network architecture, the morphology heterogeneity is heavily overlooked. Although some shape-aware operators like deformable convolution and dynamic snake convolution have been introduced to cater to specific object morphology, they still struggle with orientation-varying strip structures, such as 24 ribs and 2 clavicles. In this paper, we propose a novel convolution plugin (DSC-AMP) for medical anatomy segmentation, which is comprised of a dynamic strip convolution (DSC) operator and an adaptive morphology perception (AMP) strategy. Specifically, the dynamic strip convolution customizes gradually varying directions and offsets for each local region, achieving dynamic striped receptive fields. Additionally, the adaptive morphology perception strategy incorporates insights from various shape-aware convolutional kernels, enabling the model to discern and integrate crucial representations corresponding to heterogeneous anatomies. Extensive experiments on two large-scale datasets demonstrate the effectiveness and superiority of the proposed approach for tackling heterogeneous medical anatomy segmentation.