# Band selection for oxygenation estimation with multispectral/hyperspectral imaging

## TL;DR

This paper introduces a novel band selection method using Monte Carlo simulations and domain adaptation to enable real-time multispectral imaging for tissue oxygenation estimation, reducing data acquisition time significantly.

## Contribution

It presents a new simulation-based band selection approach combined with domain adaptation, improving tissue oxygenation estimation with fewer spectral bands.

## Key findings

- 10-20 bands suffice to replicate 101-band spectral measurements.
- Domain adaptation improves results using unlabeled in vivo data.
- Method enables faster multispectral imaging for interventional medicine.

## Abstract

Multispectral imaging provides valuable information on tissue composition such as hemoglobin oxygen saturation. However, the real-time application of this technique in interventional medicine can be challenging due to the long acquisition times needed for large amounts of hyperspectral data with hundreds of bands. While this challenge can partially be addressed by choosing a discriminative subset of bands, the band selection methods proposed to date are mainly restricted by the availability of often hard to obtain reference measurements. We address this bottleneck with a new approach to band selection that leverages highly accurate Monte Carlo (MC) simulations. We hypothesize that a so chosen small subset of bands can reproduce or even improve upon the results of a quasi continuous spectral measurement. We further investigate whether novel domain adaptation techniques can address the inevitable domain shift stemming from the use of simulations. Initial results based on in silico and in vivo experiments suggest that 10-20 bands are sufficient to closely reproduce results from spectral measurements with 101 bands in the 500-700 nm range. The investigated domain adaptation technique, which only requires unlabeled in vivo measurements, yielded better results than the pure in silico band selection method. Overall, our method could guide development of fast multispectral imaging systems suited for interventional use without relying on complex hardware setups or manually labeled data