{"database":"bioimages","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"submitter":[null],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-BIAD1197"],"repository":["bioimages"],"figure_sub":["Specimen","Study Component","organisation","Biosample","Associations","Image acquisition"],"pubmed_authors":["Shan Zhao","Johannes C. Paetzold","Harsharan Singh Bhatia","Ali Erturk","Doris Kaltenecker","Ying Chen","Mihail Todorov"],"additional_accession":[]},"is_claimable":false,"name":"3D light-sheet microscopy data for SELMA3D 2024 challenge - Training subset with no annotations - whole brain images","description":"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. ","dates":{"release":"2024-06-06T00:00:00Z","modification":"2024-11-28T21:15:09.916Z","creation":"2024-06-04T18:55:18.451Z"},"accession":"S-BIAD1197","cross_references":{}}