3D light-sheet microscopy data for SELMA3D 2024 challenge - Training subset with annotations

This dataset is the training set with 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 comtains 3D image patches of different labeled biological structures in the brain, including blood vessels, c-Fos labeled brain cells involved in neural activity, cell nuclei, and Alzheimer's disease plaques. Each patch includes corresponding pixel-wise annotations for the labeled structures.