标题： 基于深度学习的从H&E染色食管腺癌全切片图像中分割血管 显示英文标题

标题： Deep Learning Based Segmentation of Blood Vessels from H&E Stained Oesophageal Adenocarcinoma Whole-Slide Images

摘要： 血液血管（BVs）在肿瘤微环境（TME）中起着关键作用，可能影响癌症的进展和治疗反应。然而，由于它们的异质性外观，手动量化苏木精和伊红（H&E）染色图像中的BVs具有挑战性且耗时。我们提出了一种构建引导图的新方法，以提高最先进的分割模型在BVs分割中的性能，引导图鼓励模型学习BVs的代表性特征。这对于计算病理学特别有益，因为在计算病理学中标记的训练数据通常有限，大型模型容易过拟合。我们有定量和定性结果来证明我们的方法在提高分割准确性方面的有效性。未来，我们计划验证该方法在各种组织类型中分割BVs，并研究细胞结构在TME中与BVs的关系。 显示英文摘要

摘要： 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.