Single color digital H&E staining with In-and-Out Net.

Autor: Chen M; University of Texas at Austin, Department of Biomedical Engineering, 107 W Dean Keeton St, Austin, 78712, TX, United States., Liu YT; University of Texas at Austin, Department of Biomedical Engineering, 107 W Dean Keeton St, Austin, 78712, TX, United States., Khan FS; University of Texas at Austin, Department of Biomedical Engineering, 107 W Dean Keeton St, Austin, 78712, TX, United States., Fox MC; The University of Texas at Austin, Division of Dermatology, Dell Medical School, 1301 Barbara Jordan Blvd #200, Austin, 78732, TX, United States., Reichenberg JS; The University of Texas at Austin, Division of Dermatology, Dell Medical School, 1301 Barbara Jordan Blvd #200, Austin, 78732, TX, United States., Lopes FCPS; The University of Texas at Austin, Division of Dermatology, Dell Medical School, 1301 Barbara Jordan Blvd #200, Austin, 78732, TX, United States., Sebastian KR; The University of Texas at Austin, Division of Dermatology, Dell Medical School, 1301 Barbara Jordan Blvd #200, Austin, 78732, TX, United States., Markey MK; University of Texas at Austin, Department of Biomedical Engineering, 107 W Dean Keeton St, Austin, 78712, TX, United States; The University of Texas MD Anderson Cancer Center, Department of Imaging Physics, 1400 Pressler Street, Houston, 77030, TX, United States., Tunnell JW; University of Texas at Austin, Department of Biomedical Engineering, 107 W Dean Keeton St, Austin, 78712, TX, United States. Electronic address: jtunnell@mail.utexas.edu.
Jazyk: angličtina
Zdroj: Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society [Comput Med Imaging Graph] 2024 Dec; Vol. 118, pp. 102468. Date of Electronic Publication: 2024 Nov 20.
DOI: 10.1016/j.compmedimag.2024.102468
Abstrakt: Digital staining streamlines traditional staining procedures by digitally generating stained images from unstained or differently stained images. While conventional staining methods involve time-consuming chemical processes, digital staining offers an efficient and low-infrastructure alternative. Researchers can expedite tissue analysis without physical sectioning by leveraging microscopy-based techniques, such as confocal microscopy. However, interpreting grayscale or pseudo-color microscopic images remains challenging for pathologists and surgeons accustomed to traditional histologically stained images. To fill this gap, various studies explore digitally simulating staining to mimic targeted histological stains. This paper introduces a novel network, In-and-Out Net, designed explicitly for digital staining tasks. Based on Generative Adversarial Networks (GAN), our model efficiently transforms Reflectance Confocal Microscopy (RCM) images into Hematoxylin and Eosin (H&E) stained images. Using aluminum chloride preprocessing for skin tissue, we enhance nuclei contrast in RCM images. We trained the model with digital H&E labels featuring two fluorescence channels, eliminating the need for image registration and providing pixel-level ground truth. Our contributions include proposing an optimal training strategy, conducting a comparative analysis demonstrating state-of-the-art performance, validating the model through an ablation study, and collecting perfectly matched input and ground truth images without registration. In-and-Out Net showcases promising results, offering a valuable tool for digital staining tasks and advancing the field of histological image analysis.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024. Published by Elsevier Ltd.)
Databáze: MEDLINE
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