Enhancing 3D Global and Local Feature Extraction for Pneumonia Multilesion Segmentation

Precise segmentation of pneumonia lesions using deep learning has been a research focus in medical image segmentation, in which convolutional neural networks (CNNs) excel at capturing local features through convolutional layers but struggle with global information, while Transformers handle global features and long-range dependencies well but require substantial computational resources and data. Motivated by the recently introduced Mamba that effectively models long-range dependencies with less complexity, we develop a novel network architecture in order to simultaneously enhance the handling of both global and local features. It integrates an enhanced Mamba module SE3DMamba to improve the extraction of three-dimensional global features and a medical version of deep residual convolution MDRConv to enhance the extraction of local features with a self-configuring mechanism. Experiments conducted on two pneumonia CT datasets, including the pneumonia multilesion segmentation dataset (PMLSegData) with three lesion types—consolidations, nodules, and cavities—and MosMedData of ground-glass opacifications demonstrate that our network surpasses state-of-the-art CNN and Transformer-based segmentation models across all tasks, advancing the clinical feasibility of deep learning for pneumonia multilesion segmentation.