Single color virtual H&E staining with In-and-Out Net
<|reference_start|>Single color virtual H&E staining with In-and-Out Net: Virtual 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, virtual staining offers an efficient and low infrastructure alternative. Leveraging microscopy-based techniques, such as confocal microscopy, researchers can expedite tissue analysis without the need for physical sectioning. However, interpreting grayscale or pseudo-color microscopic images remains a challenge 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, specifically designed for virtual 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. We enhance nuclei contrast in RCM images using aluminum chloride preprocessing for skin tissues. Training the model with virtual H\&E labels featuring two fluorescence channels eliminates the need for image registration and provides 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 virtual staining tasks and advancing the field of histological image analysis.<|reference_end|>
@article{chen2024single,
    title={Single color virtual H&E staining with In-and-Out Net},
    author={Mengkun Chen and Yen-Tung Liu and Fadeel Sher Khan and Matthew C. Fox
  and Jason S. Reichenberg and Fabiana C.P.S. Lopes and Katherine R. Sebastian
  and Mia K. Markey and James W. Tunnell},
    journal={arXiv preprint arXiv:2405.13278},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.13278},
    primaryClass={cs.CV physics.med-ph}
}