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 pursue. Here, we 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 seconds with 1.1-um lateral resolution. Moreover, the CHAMP image can be transformed into a virtually stained histological image (Deep-CHAMP) through unsupervised learning within 15 seconds, where significant cellular features are quantitatively extracted with high accuracy. The versatility of CHAMP is experimentally demonstrated using mouse brain/kidney 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.

A Weakly Supervised Deep Generative Model for Complex Image Restoration and Style Transformation

“…Inc.). A deep ultraviolet lightemitting diode of 265 nm (M265L4, Thorlabs Inc.) was used as an excitation light source because of its high absorption in cell nuclei [28], consequently providing high nuclear contrast without labels [29]. After acquiring the autofluorescence image, the same slide was stained with H&E and its bright-field images were captured using a whole-slide scanner equipped with a 20X/0.75 NA objective lens (NanoZoomer-SQ, Hamamatsu Photonics K.K).…”

Slide-free histological imaging by microscopy with ultraviolet surface excitation using speckle illumination

“…The reconstruction was performed in each channel, respectively, based on the framework in Ref. [19]. A momentum-accelerated ptychographical engine [20] was adopted, which allows quick update and regularization when the acquired image is susceptible to noise, enabling high robustness to changes in frame intensities due to photobleaching.…”

Rapid and label-free histological imaging of unprocessed surgical tissues via dark-field reflectance ultraviolet microscopy

“… 13 , 14 , 15 The contrast in ultraviolet photoacoustic microscopy (UV-PAM) arises from intrinsic light absorption, which enables high-contrast imaging of cell nuclei. 16 , 17 , 18 Nonlinear microscopy, including multiphoton microscopy and stimulated Raman scattering microscopy, can generate optical signals from different sources, such as autofluorescence emission, 19 , 20 , 21 noncentrosymmetric structures, 22 , 23 tissue interfaces with a change in refractive indices, 24 , 25 and molecular vibrations. 26 , 27 , 28 However, these scanning-based methods have limited imaging throughput and cannot meet the requirements for rapid pathological tissue assessment.…”