High‐Throughput, Label‐Free and Slide‐Free Histological Imaging by Computational Microscopy and Unsupervised 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 mm2/10 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.

Unsupervised Hyperspectral Microscopic Image Segmentation Using Deep Embedded Clustering Algorithm

“…Methods for unsupervised learning (b) selecting acceptable and efficient deep learning methodologies and issues to verify and confirm the research findings (d) investigating the best deep learning strategy for data classification. This section's subjects elaborate on the research's goal [ 3 – 5 ].…”

Three-dimensional label-free histological imaging of whole organs by microtomy-assisted autofluorescence tomography

“…In addition, this objective lens can achieve the optimal imaging quality because the DOF matches the optical sectioning thickness provided by the UV surface excitation. The lateral resolution can be further improved by our recently proposed method with computational microscopy ( Zhang et al., 2021a ) at the expense of the increased computational burden. For axial direction, UV absorption from the embedded tissues is the current limiting factor for axial resolution, which is measured to be ∼10 μm in our experiments.…”

“…By integrating with structured illumination microscopy ( Gong et al., 2016 ), or doping the sample embedding media with strong UV-absorbing dyes ( Guo et al., 2019 ), the axial resolution is expected to be enhanced by an order of magnitude, facilitating diverse applications that require high voxel resolution such as long-range axon tracking and capillary network mapping in whole-brain imaging. Furthermore, virtually stained MATE images can be generated with the assistance of unsupervised learning ( Tschuchnig et al., 2020 ; Zhang et al., 2021a ), eliminating any training for pathologists in image interpretation for diagnostic decision-making.…”

Label-free and non-destructive pathology of human lung adenocarcinomas with ultraviolet single-plane illumination microscopy

“…However, MUSI significantly strengthens the contrast between nucleic acid and extracellular matrix with deep-UV absorption, thus showing higher consistency with the histochemical staining in routine pathological practice. Due to this reseason, our images can be easily virtually stained to mimic the appearance of the H&E-stained images via deep learning-based style transfer frameworks [25], [35] (Fig. S7).…”

“…Our recently proposed method, termed computational high-throughput autofluorescence microscopy by pattern illumination (CHAMP) [25], enables rapid, high-resolution, and label-free histological imaging of thick and unprocessed tissues, particularly favoring the applications of intraoperative SMA where immediate feedback should be provided to surgeons for optimal adjuvant treatment. However, the depth-of-focus (DOF) of CHAMP is restricted to 80 μm, which is not sufficient to accommodate large surface irregularities presented in manually-cut tissues, causing resection margins to come in and out of focus during imaging.…”