Deep-learning-based accurate hepatic steatosis quantification for histological assessment of liver biopsies.
Deep-learning-based accurate hepatic steatosis quantification for histological assessment of liver biopsies.
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DOI：
10.1038/s41374-020-0463-y
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发表时间：
2020-10
期刊：
影响因子：
--
通讯作者：
Kong J
中科院分区：
文献类型：
--
作者：
Roy M;Wang F;Vo H;Teng D;Teodoro G;Farris AB;Castillo-Leon E;Vos MB;Kong J
组织学活检肝脂肪变性液滴定量对于脂肪肝患者的风险分层和管理以及决定使用供体肝脏进行移植具有很高的临床意义。然而，由于脂肪变性实例的压倒性的大量和显著变化的外观，当手动进行时，病理检查过程受到高的阅片者间和阅片者内的可变性的影响。同时，该过程具有挑战性，因为存在大量重叠的脂肪变性液滴，其具有缺失或弱边界。在这项研究中，我们提出了一种基于深度学习的区域边界集成网络，用于对整个载玻片肝脏组织病理学图像进行精确的脂肪变性量化。该模型由两个连续的步骤组成：一个区域提取和一个边界预测模块的前景区域和脂肪变性的边界预测，其次是一个集成的预测地图生成。接下来从预测图中恢复缺失的脂肪变性边界，并从相邻的图像块中组装以生成整个载玻片组织病理学图像的结果。所得的脂肪变性测量在像素水平和脂肪变性对象水平两者上呈现与病理学家注释、放射学读数和临床数据的强相关性。此外，分离的脂肪变性对象计数被示出为在像素水平上的传统度量的有希望的替代度量。这些结果表明，AI辅助技术具有很高的潜力，可以使用整个载玻片图像来增强肝病决策支持。
Hepatic steatosis droplet quantification with histology biopsies has high clinical significance for risk stratification and management of patients with fatty liver diseases and in the decision to use donor livers for transplantation. However, pathology reviewing processes, when conducted manually, are subject to a high inter- and intra-reader variability, due to the overwhelmingly large number and significantly varying appearance of steatosis instances. Meanwhile, this process is challenging as there is a large number of overlapped steatosis droplets with either missing or weak boundaries. In this study, we propose a deep learning based region-boundary integrated network for precise steatosis quantification with whole slide liver histopathology images. The proposed model consists of two sequential steps: a region extraction and a boundary prediction module for foreground regions and steatosis boundary prediction, followed by an integrated prediction map generation. Missing steatosis boundaries are next recovered from the predicted map and assembled from adjacent image patches to generate results for the whole slide histopathology image. The resulting steatosis measures both at the pixel level and steatosis object level present strong correlation with pathologist annotations, radiology readouts and clinical data. In addition, the segregated steatosis object count is shown as a promising alternative measure to the traditional metrics at the pixel level. These results suggest a high potential of AI assisted technology to enhance liver disease decision support using whole slide images.
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