Deep learning-based virtual staining, segmentation, and classification in label-free photoacoustic histology of human specimens.

ABSTRACT: In pathological diagnostics, histological images highlight the oncological features of excised specimens, but they require laborious and costly staining procedures. Despite recent innovations in label-free microscopy that simplify complex staining procedures, technical limitations and inadequate histological visualization are still problems in clinical settings. Here, we demonstrate an interconnected deep learning (DL)-based framework for performing automated virtual staining, segmentation, and classification in label-free photoacoustic histology (PAH) of human specimens. The framework comprises three components: (1) an explainable contrastive unpaired translation (E-CUT) method for virtual H&E (VHE) staining, (2) an U-net architecture for feature segmentation, and (3) a DL-based stepwise

SUBMITTER: Yoon C 

PROVIDER: S-EPMC11369251 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature