VSGD-Net: Virtual Staining Guided Melanocyte Detection on Histopathological Images
Kechun Liua, Beibin Lia b, Wenjun Wua, Caitlin J. Mayc, Oliver H. Changa d, Stevan R. Knezeviche, Lisa M. Reischa, Joann G. Elmoref g, Linda G. Shapiroa
- aUniversity of Washington
- bMicrosoft Research
- cDermatopathology Northwest
- dVA Puget Sound
- ePathology Associates
- fUniversity of California,Los Angeles
- gUniversity of California, Los Angeles
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Abstract
Detection of melanocytes serves as a critical prerequisite in assessing melanocytic growth patterns when diagnosing melanoma and its precursor lesions on skin biopsy specimens. However, this detection is challenging due to the visual similarity of melanocytes to other cells in routine Hematoxylin and Eosin (H&E) stained images, leading to the failure of current nuclei detection methods. Stains such as Sox10 can mark melanocytes, but they require an additional step and expense and thus are not regularly used in clinical practice. To address these limitations, we introduce VSGD-Net, a novel detection network that learns melanocyte identification through virtual staining from H&E to Sox10. The method takes only routine H&E images during inference, resulting in a promising approach to support pathologists in the diagnosis of melanoma. To the best of our knowledge, this is the first study that investigates the detection problem using image synthesis features between two distinct pathology stainings. Extensive experimental results show that our proposed model outperforms state-of-the-art nuclei detection methods for melanocyte detection. The source code and pre-trained model are available at: https://github.com/kechunl/VSGD-Net.
TL;DRVSGD-Net is introduced, a novel detection network that learns melanocyte identification through virtual staining from H&E to Sox10 using image synthesis features between two distinct pathology stainings, and outperforms state-of-the-art nuclei detection methods for melanocyte detection. (Semantic Scholar)