3D light-sheet microscopy data for SELMA3D 2024 challenge - Training subset with no annotations - whole brain images

This dataset is the training set containing whole-brain images without annotations for the SELMA3D challenge. The SELMA3D challenge focuses on self-supervised learning for 3D light-sheet microscopy image segmentation. Its objective is to encourage the development of self-supervised learning methods for general segmentation of various structures in 3D light-sheet microscopy images. The dataset contains 3D whole-brain microscopy image of different labeled biological structures, including blood vessels, c-Fos labeled brain cells involved in neural activity, cell nuclei, and Alzheimer's disease plaques.