Abstract:

[Objective] With the growing importance of pathological image analysis in medicine, this study aims to develop an efficient segmentation method for nasopharyngeal carcinoma pathological images to enhance segmentation accuracy and robustness. [Methods] This study proposes a segmentation network, SELA-CSNet, which integrates attention mechanisms and channel optimization modules. The exponential linear residual attention unit, incorporating the ELU activation function, enhances the nonlinear capability of feature representation and stabilizes gradient propagation. Meanwhile, the compression excitation module optimizes the network's focus on key features by adaptively adjusting channel weights. [Results] Experiments demonstrate that the proposed network architecture achieves a Dice coefficient of 89.8%, significantly outperforming traditional residual networks and single attention mechanisms. It also exhibits superior performance in other metrics such as IoU and precision. [Conclusions] SELA-CSNet surpasses other state-of-the-art algorithms in various metrics on the current datasets, demonstrating strong application potential. Although its generalizability across multi-center datasets requires further validation, it provides a significant reference for pathological image analysis.

Key words: nasopharyngeal carcinoma, tumor microenvironment, image segmentation, exponential linear residual network, squeeze-and-excitation module, attention mechanism