SLIM Brain Database: A Multimodal Image Database of In-vivo Human Brains for Tumour Detection

Abstract Hyperspectral imaging (HSI) and machine learning (ML) have been employed in the medical field for classifying highly infiltrative brain tumors. Although existing HSI databases of in-vivo human brains are available, they present two main deficiencies. Firstly, the amount of labeled data is scarce and secondly, 3D-tissue information is unavailable. To address both issues, we present the SLIM Brain database, a multimodal image database of in-vivo human brains which provides HS brain tissue data within the 400-1000 nm spectrum, as well as RGB, depth and multi-view images. Two HS cameras, two depth cameras and different RGB sensors were used to capture images and videos from 193 patients. All data in the SLIM Brain database can be used in a variety of ways, for example to train ML models with more than 1 million HS pixels available and labeled 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