Hyperspectral (HS) imaging (HSI) is a powerful image technique that allows capturing spatial and spectral information, being able to characterize materials, tissues, and elements in a non-invasive manner. HSI technology is well established at the macroscopic level, but there are still technical challenges to overcome before it can be applied to the microscopic world, such as the lack of standardized characterization methodologies to HS microscopic systems that allow the correct data acquisition as well as ensure the repeatability of the experiments. In this work, we propose a comprehensive roadmap for characterizing and validating such systems, integrating essential parameters highlighted in the current state of the art. Furthermore, we provide a list of the materials needed for their characterization and testing the methodology on two different HS microscopic systems chosen as representative of common configurations in the field, where a HS camera is integrated into a bright-field microscope. Our proposed roadmap assesses the following parameters: dynamic range, noise quantification, pixel size, spatial frequency response, spatial scanning accuracy, spatial repeatability, flat-field correction, tone transfer, and spectral sensitivity. We address the challenge of unifying these parameters into a unified and standardized roadmap. All data used to characterize both systems have been captured by the authors. In summary, this comprehensive analysis provides a guideline for the scientific community to develop and characterize HS microscopic systems to ensure reliability, efficiency, and accuracy.

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.

Metastasis occurs in nearly 1 out of 3 breast cancer (BC) patients and significantly reduces survival rates, particularly in cases of distant metastases. As most distant metastases develop after diagnosis (i.e., recurrence) and remain incurable, there is a critical need for prognostic biomarkers to assess recurrence risk. Multimodal data analysis has emerged as a promising approach to integrate diverse information, offering a more comprehensive perspective. This study introduces the Histology HSI-BC (hyperspectral imaging - breast cancer) Recurrence Database, the first publicly accessible multimodal database designed to advance BC distant recurrence prediction. The database comprises 47 histopathological whole-slide images, 677 hyperspectral (HS) images, and clinical and demographic data from 47 BC patients, of whom 22 (47%) experienced distant recurrence over a 12-year follow-up. Histopathological slides were digitized using a whole-slide scanner and annotated by expert pathologists, while HS images were acquired with an HS camera coupled to a bright-field microscope. This database provides a promising resource for studying BC recurrence prediction and personalized treatment strategies by integrating the aforementioned multimodal data.