Pix2Pix-driven virtual pathology staining based on phase contrast microscopy: A low-cost strategy for rapid intraoperative assessment of breast cancer
Year: 2025
Type: article
Abstract: Intraoperative pathological assessment requires rapid, high-quality stained sections to support surgical decisions. Currently, however, this area lacks cost-effective and rapid staining methods for intraoperative procedures. This study integrated a low-cost phase contrast microscope (PCM) imaging and Pix2Pix Generative Adversarial Network (GAN) to take the microimages and create a virtual staining framework of intraoperative sample-frozen sections for converting label-free phase contrast images (PCIs) into H&E-stained equivalents for the first time. PCIs and H&E-stained whole-slide images were captured from sample-frozen sections of 15 registered patients with breast cancer and compared with their autofluorescence image results. Pix2Pix and CycleGAN models were trained on these images. It’s found that the Pix2Pix GAN excels in PCI and generates a clear nuclear boundary and accurate tissue structure without staining artifacts. Quantitatively, Pix2Pix model provides a higher structural similarity index (SSIM [Formula: see text] 0.57) and peak signal-to-noise ratio (PSNR [Formula: see text] 21.65) in phase contrast mode than CycleGAN in the comparison study. The virtual staining results of PCM integrated with Pix2Pix are clearly superior to those of autofluorescence with Pix2Pix, as well as with CycleGAN. Additionally, the approach offers fast processing (1.5[Formula: see text]s/mm[Formula: see text] and can generate a standard WSI virtual stained section in 3 min. This low-cost virtual staining approach bypasses the cumbersome H&E staining process in conventional pathology, significantly accelerating diagnostic procedures. It’s very possible and of significance for this intelligent intraoperative technology to be extended to other intraoperative pathological assessment scenarios requiring rapid histological assessment.
Authors: Yunhong Li, Jianbo Zhu, Huijie Wang, Jing Pei, Zichen Ling, Sheng Ding, Linwei Shang, Jianhua Yin
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,500
Funders: National Natural Science Foundation of China, Jiangsu Provincial Key Research and Development Program, Program for Jiangsu Excellent Scientific and Technological Innovation Team, Scientific Research Foundation of Education Department of Anhui Province of China, National Outstanding Youth Science Fund Project of National Natural Science Foundation of China, Affiliated Hospital of Southwest Medical University, Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space
OpenAlex ID: W4417107329