E2MISeg: Enhancing edge-aware 3D medical image segmentation via feature progressive co-aggregation

作者：Lincen Jiang, William Xu, Xinyuan Zheng, Zitong Zhang, Zekun Jiang, Chong Jiang, Yanli Chen, Yimu Ji, Shangdong Liu, Jian-wei Liu, Jingyan Xu · 发表于：Expert Systems with Applications · 年份：2025 · DOI：10.1016/j.eswa.2025.128861 · 被引用次数：4 · 研究领域：Brain Tumor Detection and Classification、Advanced Neural Network Applications、Medical Imaging and Analysis

• We propose a novel enhancing edge-aware neural network for multi-modal 3D medical image segmentation. • A feature progressive co-aggregation strategy for improving feature representation and edge voxel classification. • Compared with the most advanced methods, our model achieves better performance and generalization ability. • We construct a challenging clinical diagnostic dataset of PET images for mantle cell lymphoma. 3D segmentation is critically essential in the clinical medical field, which aids physicians in locating lesions and assists in clinical decision-making. The unique properties of organ and tumour images with large-scale variations and low-edge pixel-level contrast make clear segment edges difficult. Facing these problems, we propose an Enhancing Edge-aware Medical Image Seg mentation (E2MISeg) for smooth segmentation in boundary ambiguity. Firstly, we propose the Multi-level Feature Group Aggregation (MFGA) module to enhance the accuracy of edge voxel classification through the boundary clue of lesion tissue and background. Secondly, to minimize the influence of background noise on the model’s sensitivity to the foreground, the Hybrid Feature Representation (HFR) block utilizes an interactive CNN and Transformer to deeply mine the lesion area and edge texture features while providing more clues for the MFGA module. Finally, we introduce the Scale-Sensitive (SS) loss function that dynamically adjusts the weights assigned to targets based on segmentation error...