振动体
计算机科学
组织学
轮廓
病理
人工智能
生物医学工程
医学
计算机图形学(图像)
免疫组织化学
作者
Lei Kang,Wen-Bei Yu,Yan Zhang,Zhenghui Chen,Terence T. W. Wong
出处
期刊:ACS Photonics
[American Chemical Society]
日期:2023-09-21
卷期号:10 (10): 3541-3550
标识
DOI:10.1021/acsphotonics.3c00536
摘要
Three-dimensional (3D) histopathology involves the microscopic examination of a specimen, which plays a vital role in studying tissue’s 3D structures and the signs of diseases. However, acquiring high-quality histological images of a whole organ is extremely time-consuming (e.g., several weeks) and laborious, as the organ has to be sectioned into hundreds or thousands of slices for imaging. Besides, the acquired images are required to undergo a complicated image registration process for 3D reconstruction. Here, by incorporating a recently developed vibratome-assisted block-face imaging technique with deep learning, we developed a pipeline termed HistoTRUST that can rapidly and automatically generate subcellular whole organ’s virtual hematoxylin and eosin (H&E) stained histological images, which can be reconstructed into 3D by simple image stacking (i.e., without registration). The performance and robustness of HistoTRUST have been successfully validated by imaging all six organs (e.g., brain, heart, liver, lung, kidney, and spleen). The imaging process for a whole organ takes hours to days, depending on the volume of imaged samples. The generated 3D dataset has the same color tune as the traditional H&E stained histological images. Therefore, the virtual H&E stained images can be directly analyzed by pathologists. HistoTRUST has a high potential to serve as a new standard in providing 3D histology for research or clinical applications.
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