Swin-WNet: Boundary-Aware Semantic Segmentation for Oral Squamous Cell Carcinoma

Zhenfei Wang, Zhenyang Zhu, Kunio Yoshizawa, Masahiro Toyoura, Naoki Oishi, Xiaoyang Mao

Abstract: Oral squamous cell carcinoma (OSCC) poses a significant threat to public health due to its severity and the laborintensive process of image analysis required by physicians. Intercellular bridges are bridge-like structures that connect adjacent cells and indicate the differentiation level of a cancer. Although intercellular bridges are known to disappear as differentiation decreases, pathologists and clinicians evaluate the presence of intercellular bridges to assess the degree of differentiation of cancer. While state-of-the-art (SOTA) deep learning methods, such as U-Net and its variants, perform well on uniform and clearly delineated objects (e.g., cell, lung, etc.), accurately segmenting intricate objects (e.g., intercellular bridge, retinal vessel) remains challenging due to their complex topologies, fine branches, and irregular morphological changes. This paper aims to propose a method that effectively utilize boundary information, particularly targeting intricate objects. Our approach, inspired by Swin-UNet, employs the Swin Transformer, comprising a feature encoder, two decoders (semantic decoder and boundary decoder) and an attention-guided fusion module to enhance the model's ability to segment intricate objects. By applying the constraints of the boundary decoder, the feature encoder's ability to encode structural information is enhanced without compromising semantic information extraction and representation. Furthermore, fusing the outputs of the boundary decoder and semantic decoder further strengthens the detail of structural information. To validate the generalizability of our method, we conducted comparative experiments on one private and one public dataset. The results demonstrate that our method outperforms SOTA methods.

External IDs:dblp:conf/cw/WangZYTOM25