# Perfusion Quantification from Endoscopic Videos: Learning to Read Tumor Signatures

arXiv: https://arxiv.org/abs/2006.14321 · HTML: https://mlsanity.com/paper/2006.14321/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jun 2020.

## Built on by (most cited first, 1 of 1)

- [A Real-Time Region Tracking Algorithm Tailored to Endoscopic Video with Open-Source Implementation](https://mlsanity.com/paper/2203.08858/) (2022)

## Abstract (arXiv)

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.