HATs: Hierarchical Adaptive Taxonomy Segmentation for Panoramic Pathology Image Analysis.

Ruining Deng, Quan Liu, Can Cui, Tianyuan Yao, Juming Xiong, Shunxing Bao, Hao Li, Mengmeng Yin, Yu Wang, Shilin Zhao, Yucheng Tang, Haichun Yang, Yuankai Huo
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Abstract

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.

全景病理图像分析的层次自适应分类分割。
全景图像分割在计算病理学提出了一个显着的挑战,由于形态复杂和可变缩放解剖。例如,肾脏病理中复杂的组织跨越多层,从皮层和髓质等区域到肾小球、小管和血管等功能单位,再到各种细胞类型。在本文中,我们提出了一种新的分层自适应分类分割(HATs)方法,该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。我们的方法需要(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跨越区域、功能单元和细胞的多功能“即插即用”损失函数;(2)将解剖层次结构和尺度考虑纳入所有全景实体的统一简单矩阵表示中;(3)采用最新的人工智能基础模型(EfficientSAM)作为特征提取工具,增强了模型的适应性,同时消除了传统分段任意模型(SAM)中手动生成提示符的需求。实验结果表明,HATs方法提供了一种高效的策略,可以将临床见解和成像先例整合到超过15个类别的统一分割模型中。官方实现可以在https://github.com/hrlblab/HATs上公开获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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