HATs：面向全景病理图像分析的分层自适应分类体系分割
HATs: Hierarchical Adaptive Taxonomy Segmentation for Panoramic Pathology Image Analysis
AI总结：
本文提出HATs方法，通过分层自适应分类体系、统一矩阵表示和EfficientSAM特征提取，实现对肾脏全景病理图像中超过15个类别的有效分割。
摘要：
计算病理学中的全景图像分割由于形态复杂且尺度多变的解剖结构而面临显著挑战。例如，肾脏病理中的复杂组织跨越多个层次，从皮质和髓质等区域，到肾小球、肾小管和血管等功能单元，再到各种细胞类型。在本文中，我们提出了一种新颖的分层自适应分类体系分割（HATs）方法，旨在通过利用详细的解剖学见解来彻底分割肾脏结构的全景视图。我们的方法包括：（1）创新的HATs技术，将15个不同对象类别之间的空间关系转化为一种通用的“即插即用”损失函数，跨越区域、功能单元和细胞；（2）将解剖层次和尺度考虑纳入一个统一且简单的矩阵表示中，用于所有全景实体；（3）采用最新的人工智能基础模型（EfficientSAM）作为特征提取工具，以提升模型的适应性，同时消除了传统分割一切模型（SAM）中手动提示生成的需要。实验结果表明，HATs方法提供了一种高效且有效的策略，将临床见解和成像先例整合到一个涵盖超过15个类别的统一分割模型中。官方实现已在https://github.com/hrlblab/HATs公开提供。
英文摘要：
Panoramic image segmentation in computational pathology presents a remarkable challenge due to the morphologically complex and variably scaled anatomy. For instance, the intricate organization in kidney pathology spans multiple layers, from regions like the cortex and medulla to functional units such as glomeruli, tubules, and vessels, down to various cell types. In this paper, we propose a novel Hierarchical Adaptive Taxonomy Segmentation (HATs) method, which is designed to thoroughly segment panoramic views of kidney structures by leveraging detailed anatomical insights. Our approach entails (1) the innovative HATs technique which translates spatial relationships among 15 distinct object classes into a versatile "plug-and-play" loss function that spans across regions, functional units, and cells, (2) the incorporation of anatomical hierarchies and scale considerations into a unified simple matrix representation for all panoramic entities, (3) the adoption of the latest AI foundation model (EfficientSAM) as a feature extraction tool to boost the model's adaptability, yet eliminating the need for manual prompt generation in conventional segment anything model (SAM). Experimental findings demonstrate that the HATs method offers an efficient and effective strategy for integrating clinical insights and imaging precedents into a unified segmentation model across more than 15 categories. The official implementation is publicly available at https://github.com/hrlblab/HATs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
