用病理大模型提升黑色素瘤组织分割精度
Leveraging Pathology Foundation Models for Panoptic Segmentation of Melanoma in H&E Images
- 用Virchow2大模型提取特征，融合原始图像进行分割
- 在PUMA挑战赛中取得第一名，五类组织分割准确
- 适合计算病理、医学图像分析研究人员参考
黑色素瘤是一种进展迅速、转移潜力高的皮肤癌，其组织形态的精准刻画对预后判断和治疗方案制定至关重要。然而，人工对苏木精-伊红（H&E）染色全幻灯片图像（WSIs）进行组织区域分割工作量大且存在观察者间差异，亟需可靠的自动化分割方法。本研究提出一种新型深度学习网络，用于黑色素瘤H&E图像中五类组织的分割。方法基于在310万张病理图像上训练的Virchow2病理基础模型作为特征提取器，将其特征与原始RGB图像融合后，输入编码器-解码器结构的分割网络（Efficient-UNet）生成精确分割图。该模型在PUMA Grand Challenge的组织分割任务中排名第一，展现出优异的性能与泛化能力。结果表明，将病理基础模型融入分割网络可有效加速计算病理工作流。
原文摘要 · Abstract (English)
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.
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