标题： 利用病理学基础模型进行H&E图像中黑色素瘤的全景分割 显示英文标题

标题： Leveraging Pathology Foundation Models for Panoptic Segmentation of Melanoma in H&E Images

摘要： 黑色素瘤是一种进展迅速且具有高转移潜力的皮肤癌。 在黑色素瘤中准确表征组织形态对于预后和治疗计划至关重要。 然而，从苏木精和伊红（H&E）染色的全切片图像（WSIs）中进行手动分割既费时又容易出现观察者之间的差异，这促使需要可靠的自动组织分割方法。 在本研究中，我们提出了一种新的深度学习网络，用于对黑色素瘤H&E图像中的五个组织类别进行分割。 我们的方法利用了Virchow2，这是一个在310万张组织病理学图像上训练的病理基础模型作为特征提取器。 这些特征与原始RGB图像融合，并随后通过编码器-解码器分割网络（Efficient-UNet）进行处理，以生成准确的分割图。 所提出的模型在PUMA大挑战的组织分割任务中获得第一名，展示了其强大的性能和泛化能力。 我们的结果表明，将病理基础模型整合到分割网络中具有潜力和有效性，可以加速计算病理学的工作流程。 显示英文摘要

摘要： Melanoma is an aggressive form of skin cancer with rapid progression and high metastatic potential. Accurate characterisation of tissue morphology in melanoma is crucial for prognosis and treatment planning. However, manual segmentation of tissue regions from haematoxylin and eosin (H&E) stained whole-slide images (WSIs) is labour-intensive and prone to inter-observer variability, this motivates the need for reliable automated tissue segmentation methods. In this study, we propose a novel deep learning network for the segmentation of five tissue classes in melanoma H&E images. Our approach leverages Virchow2, a pathology foundation model trained on 3.1 million histopathology images as a feature extractor. These features are fused with the original RGB images and subsequently processed by an encoder-decoder segmentation network (Efficient-UNet) to produce accurate segmentation maps. The proposed model achieved first place in the tissue segmentation task of the PUMA Grand Challenge, demonstrating robust performance and generalizability. Our results show the potential and efficacy of incorporating pathology foundation models into segmentation networks to accelerate computational pathology workflows.