Abstract
Hyperspectral imaging (HSI) and machine learning (ML) have been employed in the medical field for classifying highly infiltrative brain tumours. Although existing HSI databases of in vivo human brains are available, they present two main deficiencies. First, the amount of labelled data are scarce, and second, 3D-tissue information is unavailable. To address both issues, we present the SLIMBRAIN database, a multimodal image database of in vivo human brains that provides HS brain tissue data within the 400-1000 nm spectra, as well as RGB, depth and multiview images. Two HS cameras, two depth cameras and different RGB sensors were used to capture images and videos from 193 patients. All the data in the SLIMBRAIN database can be used in a variety of ways, for example, to train ML models with more than 1 million HS pixels available and labelled by neurosurgeons, to reconstruct 3D scenes or to visualize RGB brain images with different pathologies, offering unprecedented flexibility for both the medical and engineering communities.