Multi-Channel Feature Extraction for Virtual Histological Staining of Photon Absorption Remote Sensing Images

TL;DR

This paper presents a deep learning framework using multi-channel feature extraction from photon absorption remote sensing images to perform virtual histological staining, aiming to improve diagnostic efficiency and reduce reliance on traditional staining methods.

Contribution

It introduces a novel multi-channel cycleGAN model that incorporates features from PARS signals, enhancing virtual staining accuracy over conventional methods.

Findings

Feature combination improves tissue structure labeling

Virtual stained images closely match standard H&E images

Method enhances intraoperative microscopy potential

Abstract

Accurate and fast histological staining is crucial in histopathology, impacting diagnostic precision and reliability. Traditional staining methods are time-consuming and subjective, causing delays in diagnosis. Digital pathology plays a vital role in advancing and optimizing histology processes to improve efficiency and reduce turnaround times. This study introduces a novel deep learning-based framework for virtual histological staining using photon absorption remote sensing (PARS) images. By extracting features from PARS time-resolved signals using a variant of the K-means method, valuable multi-modal information is captured. The proposed multi-channel cycleGAN (MC-GAN) model expands on the traditional cycleGAN framework, allowing the inclusion of additional features. Experimental results reveal that specific combinations of features outperform the conventional channels by improving the labeling of tissue structures prior to model training. Applied to human skin and mouse brain tissue, the results underscore the significance of choosing the optimal combination of features, as it reveals a substantial visual and quantitative concurrence between the virtually stained and the gold standard chemically stained hematoxylin and eosin (H&E) images, surpassing the performance of other feature combinations. Accurate virtual staining is valuable for reliable diagnostic information, aiding pathologists in disease classification, grading, and treatment planning. This study aims to advance label-free histological imaging and opens doors for intraoperative microscopy applications.