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.
@article{arxiv.2006.14321,
title = {Perfusion Quantification from Endoscopic Videos: Learning to Read Tumor Signatures},
author = {Sergiy Zhuk and Jonathan P. Epperlein and Rahul Nair and Seshu Thirupati and Pol Mac Aonghusa and Ronan Cahill and Donal O'Shea},
journal= {arXiv preprint arXiv:2006.14321},
year = {2020}
}
Comments
To be published in 23rd International Conference on Medical Image Computing & Computer Assisted Intervention (MICCAI 2020)