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arXiv · 2006.14321

Perfusion Quantification from Endoscopic Videos: Learning to Read Tumor Signatures

Sergiy Zhuk·Jonathan P. Epperlein·Rahul Nair·Seshu Thirupati·Pol Mac Aonghusa·Ronan Cahill·Donal O'Shea
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Abstract

Intra-operative identification of malignant versus benign or healthy tissue is a major challenge in fluorescence guided cancer surgery. We propose a perfusion quantification method for computer-aided interpretation of subtle differences in dynamic perfusion patterns which can be used to distinguish between normal tissue and benign or malignant tumors intra-operatively in real-time by using multispectral endoscopic videos. The method exploits the fact that vasculature arising from cancer angiogenesis gives tumors differing perfusion patterns from the surrounding tissue, and defines a signature of tumor which could be used to differentiate tumors from normal tissues. Experimental evaluation of our method on a cohort of colorectal cancer surgery endoscopic videos suggests that the proposed tumor signature is able to successfully discriminate between healthy, cancerous and benign tissue with 95% accuracy.

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Sergiy Zhuk, Jonathan P. Epperlein, Rahul Nair, Seshu Thirupati, Pol Mac Aonghusa, Ronan Cahill, Donal O'Shea. 2020-06-25. Perfusion Quantification from Endoscopic Videos: Learning to Read Tumor Signatures. https://arxiv.org/abs/2006.14321

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