标题： 基于深度学习的食管腺癌 H&E 染色全切片图像血管分割 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： 血管 (BV) 在肿瘤微环境 (TME) 中发挥着至关重要的作用，可能影响癌症进展和治疗反应。 然而，由于 BV 外观的异质性，手动量化苏木精和伊红 (H&E) 染色图像中的 BV 极具挑战性且耗费人力。我们提出了一种构建引导图的新方法，以提升最先进的 BV 分割模型的性能。引导图鼓励模型学习 BV 的代表性特征。这对于计算病理学尤为有益，因为标记训练数据通常有限，大型模型容易过度拟合。我们已获得定量和定性结果，以证明该方法在提高分割精度方面的有效性。未来，我们计划验证该方法在不同组织类型中分割 BV 的效果，并研究细胞结构在 TME 中与 BV 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.