Perfusion Quantification from Endoscopic Videos: Learning to Read Tumor Signatures
This addresses the challenge of real-time tissue differentiation during surgery for cancer patients, representing a domain-specific incremental improvement.
The paper tackled the problem of intra-operative identification of malignant versus benign or healthy tissue in cancer surgery by proposing a perfusion quantification method from endoscopic videos, achieving 95% accuracy in discriminating between healthy, cancerous, and benign tissue.
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.