Extracting, Visualizing, and Learning from Dynamic Data: Perfusion in Surgical Video for Tissue Characterization

TL;DR

This paper presents a method to analyze early fluorescence signals after ICG administration in surgical videos, aiding tissue characterization and differentiating cancerous from benign lesions.

Contribution

It introduces a novel approach to extract and utilize dynamic fluorescence data from surgical videos for tissue characterization.

Findings

Effective signal processing method developed for early ICG fluorescence

Initial results show potential in differentiating cancerous and benign tissues

Method is being validated in multicenter clinical studies

Abstract

Intraoperative assessment of tissue can be guided through fluorescence imaging which involves systemic dosing with a fluorophore and subsequent examination of the tissue region of interest with a near-infrared camera. This typically involves administering indocyanine green (ICG) hours or even days before surgery and intraoperative visualization at the time predicted for steady-state signal-to-background status. Here, we describe our efforts to capture and utilize the information contained in the first few minutes after ICG administration from the perspective of both signal processing and surgical practice. We prove a method for characterization of cancerous versus benign rectal lesions now undergoing further development and validation via multicenter clinical phase studies.