A carving hierarchical information integration network for medical image segmentation

Abstract

Semantic segmentation techniques are widely applied in various image analysis tasks. However, compared with natural images, medical image segmentation presents greater challenges. For instance, lesions often vary significantly in morphology, size, and structure, and are frequently accompanied by low contrast and blurred boundaries. To simultaneously preserve fine tissue structures when handling large-scale lesions and ensure the coherence of the divergent structures of vessels, tumors, and other organs while accurately segmenting adjacent cells, this paper proposes the concept of “Global Capture and Local Carving”. It introduces a model that integrates a hierarchical information fusion strategy, named CarveNet. CarveNet incorporates a carving mechanism at three levels: downsampling, feature transmission, and bottleneck processing. Structural Carving Pooling Module underpins the downsampling carving, deeply optimizing the information structure and morphology at different levels to maximize detail retention and minimize downsampling loss. Multi-window Carving ViT is employed for transmission carving, enhancing global information modeling while refining local feature representation. The bottleneck carving integrates a long-distance recurrent communication mechanism with grid-like spatial random shuffling to strengthen the robustness and diversity of feature extraction. Experiments conducted on eight medical image datasets demonstrate that CarveNet consistently delivers outstanding performance across all tasks, surpassing the second-best method in Dice coefficient by 1.136 %. This fully validates its effectiveness in terms of multi-lesion adaptability, accuracy, and generalization capability. The code is available at https://github.com/YF-W/CarveNet.