1. High-Throughput, Label-Free and Slide-Free Histological Imaging by Computational Microscopy and Unsupervised Learning.
- Author
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Zhang Y, Kang L, Wong IHM, Dai W, Li X, Chan RCK, Hsin MKY, and Wong TTW
- Subjects
- Animals, Humans, Mice, Models, Animal, Brain cytology, Histological Techniques methods, Kidney cytology, Lung cytology, Microscopy methods, Unsupervised Machine Learning
- Abstract
Rapid and high-resolution histological imaging with minimal tissue preparation has long been a challenging and yet captivating medical pursuit. Here, the authors propose a promising and transformative histological imaging method, termed computational high-throughput autofluorescence microscopy by pattern illumination (CHAMP). With the assistance of computational microscopy, CHAMP enables high-throughput and label-free imaging of thick and unprocessed tissues with large surface irregularity at an acquisition speed of 10 mm
2 /10 s with 1.1-µm lateral resolution. Moreover, the CHAMP image can be transformed into a virtually stained histological image (Deep-CHAMP) through unsupervised learning within 15 s, where significant cellular features are quantitatively extracted with high accuracy. The versatility of CHAMP is experimentally demonstrated using mouse brain/kidney and human lung tissues prepared with various clinical protocols, which enables a rapid and accurate intraoperative/postoperative pathological examination without tissue processing or staining, demonstrating its great potential as an assistive imaging platform for surgeons and pathologists to provide optimal adjuvant treatment., (© 2021 The Authors. Advanced Science published by Wiley-VCH GmbH.)- Published
- 2022
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