Perfusion Quantification from Endoscopic Videos: Learning to Read Tumor Signatures

• URL: http://arxiv.org/abs/2006.14321v1
• Date: Thu, 25 Jun 2020 11:53:20 GMT
• Title: Perfusion Quantification from Endoscopic Videos: Learning to Read Tumor Signatures
• Authors: Sergiy Zhuk, Jonathan P. Epperlein, Rahul Nair, Seshu Thirupati, Pol Mac Aonghusa, Ronan Cahill, Donal O'Shea
• Abstract summary: We propose a perfusion quantification method for computer-aided interpretation of subtle differences in dynamic perfusion patterns. The method exploits the fact that vasculature arising from cancer angiogenesis gives tumors differing perfusion patterns from the surrounding tissue. 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.
• Score: 3.5769263034973697
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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