High-to-Low Spectral Mapping for Cross-System Feature Adaptation in Medical Hyperspectral Imaging

Abstract

Hyperspectral (HS) imaging has proven to be a promising intraoperative tool for tissue discrimination. However, obtaining representative datasets for intraoperative imaging remains challenging due to the complexity of surgical workflows and the sensitivity of the operating environments. Hence, developing new methods for cross-system feature adaptation could address this limitation. This work proposes a method for mapping high-resolution spectral data into lower-resolution sensor-conditioned domains, generating synthetic HS data that replicate the spectral features of the target system. We assessed the mapped data using public HS datasets and quantified spectral similarities using different metrics. Additionally, we evaluated the method with a HS classification framework for an intraoperative brain tumour classification problem. Results demonstrate that the synthetic data achieve high spectral alignment to original and actual data, captured with the target system. The brain tumour classification results show comparable performance between data modalities. Overall, this work provides a way to adapt existing HS datasets to complement newly acquired data, accelerating the development of artificial intelligence algorithms. This is particularly relevant in medical research, and especially in neurosurgery, where the complexity of acquisition environments limits the collection of large datasets.