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Perfusion Quantification from Endoscopic Videos: Learning to Read Tumor Signatures

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Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 (MICCAI 2020)

Abstract

Intra-operative (this work was partially supported by Disruptive Technologies Innovation Fund, Ireland, project code DTIF2018 240 CA) 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 by using multispectral endoscopic videos. The method exploits the fact that vasculature arising from cancer angiogenesis gives tumors differing perfusion patterns from the surrounding normal tissues. Experimental evaluation of our method on a cohort of colorectal cancer surgery endoscopic videos suggests that it discriminates between healthy, cancerous and benign tissues with 95% accuracy.

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Notes

  1. 1.

    Perfusion is the passage of fluid through the circulatory or lymphatic system to a capillary bed in tissue.

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Correspondence to Sergiy Zhuk .

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Zhuk, S. et al. (2020). Perfusion Quantification from Endoscopic Videos: Learning to Read Tumor Signatures. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12263. Springer, Cham. https://doi.org/10.1007/978-3-030-59716-0_68

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  • DOI: https://doi.org/10.1007/978-3-030-59716-0_68

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