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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2405.13278 |
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| _version_ | 1866913582998880256 |
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| author | Chen, Mengkun Liu, Yen-Tung Khan, Fadeel Sher Fox, Matthew C. Reichenberg, Jason S. Lopes, Fabiana C. P. S. Sebastian, Katherine R. Markey, Mia K. Tunnell, James W. |
| author_facet | Chen, Mengkun Liu, Yen-Tung Khan, Fadeel Sher Fox, Matthew C. Reichenberg, Jason S. Lopes, Fabiana C. P. S. Sebastian, Katherine R. Markey, Mia K. Tunnell, James W. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_13278 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Single color digital H&E staining with In-and-Out Net Chen, Mengkun Liu, Yen-Tung Khan, Fadeel Sher Fox, Matthew C. Reichenberg, Jason S. Lopes, Fabiana C. P. S. Sebastian, Katherine R. Markey, Mia K. Tunnell, James W. Computer Vision and Pattern Recognition Medical Physics 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. |
| title | Single color digital H&E staining with In-and-Out Net |
| topic | Computer Vision and Pattern Recognition Medical Physics |
| url | https://arxiv.org/abs/2405.13278 |