Daira Viškere, Mindaugas Tamošiūnas, Romans Maļiks, Diāna Dupļevska, Ilze Matīse-van Houtana, Roberts Kadiķis, Blaž Cugmas. Virtual Staining From Optical Coherence Tomography to Hematoxylin and Eosin Stained Skin Tumor Samples in Pets. 31st Annual International Scientific Conference "Research for Rural Development 2025, 2025.
Bibtex citation:
Bibtex citation:
@article{19813_2025,
author = {Daira Viškere and Mindaugas Tamošiūnas and Romans Maļiks and Diāna Dupļevska and Ilze Matīse-van Houtana and Roberts Kadiķis and Blaž Cugmas},
title = {Virtual Staining From Optical Coherence Tomography to Hematoxylin and Eosin Stained Skin Tumor Samples in Pets},
journal = {31st Annual International Scientific Conference "Research for Rural Development 2025},
year = {2025}
}
author = {Daira Viškere and Mindaugas Tamošiūnas and Romans Maļiks and Diāna Dupļevska and Ilze Matīse-van Houtana and Roberts Kadiķis and Blaž Cugmas},
title = {Virtual Staining From Optical Coherence Tomography to Hematoxylin and Eosin Stained Skin Tumor Samples in Pets},
journal = {31st Annual International Scientific Conference "Research for Rural Development 2025},
year = {2025}
}
Abstract: Histopathological biopsies with hematoxylin and eosin–stained (H&E) samples are gold standard in veterinary oncology. Since biopsy sampling is an invasive process and requires sedation of the animal, as well as sample preparation and obtaining results is time-consuming, other effective solutions are being sought. Optical coherence tomography (OCT) is known as non-invasive imaging technology that produces detail, cross-sectional images of tissues at a microscopic level and provide real-time images. In our research we trained neural network to produce virtually stained histopathological images from OCT images taken from lipomas, soft tissue sarcomas and mast cell tumors from dogs and cats. As there are no previous research done on recognizing different normal and tumor structures in veterinary skin tumor samples, we found that tumor borders, hair follicles can be recognized using noninvasive OCT. As reference for virtually stained image production H&E sections were used. Multiple 1x1 mm sections of input images were taken from different (normal and tumor tissue) regions. We trained our neural network using paired dataset and got first virtually stained H&E images. By modifying these algorithms for transfer learning we could improve future images making more important histological structures to be recognized and improving image quality allowing veterinarians make tumor diagnostics much faster.