Song Caifang, Huang Xiangsheng, Lyu Xiangyu
School of Mathematics and Computer Science, Shanxi Normal University, Taiyuan 030031, China.
Hebei Key Laboratory of Cognitive Intelligence, Xiong'an Institute of Innovation, Xiong'an New Area 071700, China.
Sensors (Basel). 2025 Mar 14;25(6):1829. doi: 10.3390/s25061829.
Accurate semantic segmentation and automatic thickness measurement of the glomerular basement membrane (GBM) can aid pathologists in carrying out subsequent pathological diagnoses. The GBM has a complex ultrastructure and irregular shape, which makes it difficult to segment accurately. We found that the shape of the GBM is striped, so we proposed an RSP model to extract both the strip and square features of the GBM. Additionally, grayscale images of the GBM are similar to those of surrounding tissues, and the contrast is low. We added an edge attention mechanism to further improve the quality of segmentation. Moreover, we revised the pixel-level loss function to consider the tissues around the GBM and locate the GBM as a doctor would, i.e., by using the tissues as the reference object. Ablation experiments with each module showed that SSPNet can better segment the GBM. The proposed method was also compared with the existing medical semantic segmentation model. The experimental results showed that the proposed method can obtain high-precision segmentation results for the GBM and completely segment the target. Finally, the thickness of the GBM was calculated using a skeleton extraction method to provide quantitative data for expert diagnosis.
肾小球基底膜（GBM）的精确语义分割和自动厚度测量有助于病理学家进行后续的病理诊断。GBM具有复杂的超微结构和不规则的形状，这使得准确分割变得困难。我们发现GBM的形状是条纹状的，因此提出了一种RSP模型来提取GBM的条纹和方形特征。此外，GBM的灰度图像与周围组织的灰度图像相似，对比度较低。我们添加了边缘注意力机制以进一步提高分割质量。此外，我们修改了像素级损失函数，以考虑GBM周围的组织，并像医生一样定位GBM，即以周围组织作为参考对象。对每个模块进行的消融实验表明，SSPNet能够更好地分割GBM。我们还将所提出的方法与现有的医学语义分割模型进行了比较。实验结果表明，所提出的方法能够获得GBM的高精度分割结果，并完全分割目标。最后，使用骨架提取方法计算GBM 的厚度，为专家诊断提供定量数据。
