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Spectral imaging enables contrast agent-free real-time ischemia monitoring in laparoscopic surgery

This is the python code implementation of the experiments presented in the manuscript titled "Spectral imaging enables contrast agent-free real-time ischemia monitoring in laparoscopic surgery". It's main components are the definitions of the deep learning model utilized for ischemia detection, the implementation of the region of interest tracking, and the code used to analyze inter-patient variability with mixed models.

By Leonardo Ayala, Tim Adler, Silvia Seidlitz, Sebastian Wirkert, Alexander Seitel, Jan Sellner, Anant Vemuri, Manuel Wiesenfarth, Nicholas Schreck, Sebastian Pirmann, Annette Kopp-Schneider, Lena Maier-HeinToolReference information

Resource information

Format
zip
Original repository
7529804
DOI
10.5281/zenodo.7529804
Credit basis
Creators in Zenodo record metadata
Access
Open files available from the original Zenodo deposit
Last checked
Nov 5, 2026
License
mit-license
Last record update
Nov 5, 2026

About this resource

This is the python code implementation of the experiments presented in the manuscript titled "Spectral imaging enables contrast agent-free real-time ischemia monitoring in laparoscopic surgery". It's main components are the definitions of the deep learning model utilized for ischemia detection, the implementation of the region of interest tracking, and the code used to analyze inter-patient variability with mixed models.