用引导图提升血管分割精度，解决病理图像标注少难题
Deep Learning Based Segmentation of Blood Vessels from H&E Stained Oesophageal Adenocarcinoma Whole-Slide Images
- 构建引导图帮助模型学习血管特征
- 在有限标注数据下显著提升分割准确率
- 适合计算病理学、肿瘤微环境研究者
血管在肿瘤微环境中起关键作用，可能影响癌症进展和治疗反应。然而，由于形态多样，手动量化苏木精-伊红（H&E）染色图像中的血管既困难又耗时。本文提出一种新方法，通过构建引导图来提升先进分割模型在血管分割上的性能，引导图促使模型学习更具代表性的血管特征。该方法在计算病理学中尤为有益，因标注数据通常有限，大模型易过拟合。我们通过定量与定性结果证明了该方法的有效性。未来计划验证该方法在多种组织类型中的适用性，并研究细胞结构与血管在肿瘤微环境中的关联。
原文摘要 · Abstract (English)
Blood vessels (BVs) play a critical role in the Tumor Micro-Environment (TME), potentially influencing cancer progression and treatment response. However, manually quantifying BVs in Hematoxylin and Eosin (H&E) stained images is challenging and labor-intensive due to their heterogeneous appearances. We propose a novel approach of constructing guiding maps to improve the performance of state-of-the-art segmentation models for BV segmentation, the guiding maps encourage the models to learn representative features of BVs. This is particularly beneficial for computational pathology, where labeled training data is often limited and large models are prone to overfitting. We have quantitative and qualitative results to demonstrate the efficacy of our approach in improving segmentation accuracy. In future, we plan to validate this method to segment BVs across various tissue types and investigate the role of cellular structures in relation to BVs in the TME.
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