Tumour classification with optimized sliding window size for OCT imaging

Oskars Čiževskis; Blaž Cugmas; Daira Viškere; Mikus Melderis; Inta Liepniece-Karele; Junjie Yao; Mindaugas Tamošiūnas

doi:10.1117/12.2607185

7 March 2022 Tumour classification with optimized sliding window size for OCT imaging

Oskars Čiževskis, Blaž Cugmas, Daira Viškere, Mikus Melderis, Inta Liepniece-Karele, Junjie Yao, Mindaugas Tamošiūnas

Proceedings Volume 11948, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVI; 119480S (2022) https://doi.org/10.1117/12.2607185
Event: SPIE BiOS, 2022, San Francisco, California, United States

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Abstract

Skin and subcutaneous tumors are widespread in dogs and cats. Current tumor diagnostics (e.g., biopsy, fineneedle cytology) is invasive and labor-consuming. In this work, we studied ex vivo the most common canine and feline tumor OCT images using sliding window analysis and linear SVC classification, and we compared different sliding window sizes to determine the most optimal window sizes when differentiating between skin, mast cell tumours and soft tissue sarcomas. Sensitivities and specificities of all tissue classes saw an increase with increasing window size at small window size values and plateaued at around 60-80 μm, indicating the most significant tissue structures for differentiation via SWA likely lay here. Our work is the first veterinary OCT study on multiple canine and feline skin tumors to optimize the sliding window size for image pattern analysis.

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Oskars Čiževskis, Blaž Cugmas, Daira Viškere, Mikus Melderis, Inta Liepniece-Karele, Junjie Yao, and Mindaugas Tamošiūnas "Tumour classification with optimized sliding window size for OCT imaging", Proc. SPIE 11948, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXVI, 119480S (7 March 2022); https://doi.org/10.1117/12.2607185

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