Automated detection of specific cells in three-dimensional datasets such as whole-brain light-sheet image stacks is challenging. Here, we present DELiVR, a virtual reality-trained deep-learning pipeline for detecting c-Fos+ cells as markers for neuronal activity in cleared mouse brains. Virtual reality annotation substantially accelerated training data generation, enabling DELiVR to outperform state-of-the-art cell-segmenting approaches. Our pipeline is available in a user-friendly Docker container that runs with a standalone Fiji plugin. DELiVR features a comprehensive toolkit for data visualization and can be customized to other cell types of interest, as we did here for microglia somata, using Fiji for dataset-specific training. We applied DELiVR to investigate cancer-related brain activity, unveiling an activation pattern that distinguishes weight-stable cancer from cancers associated with weight loss. Overall, DELiVR is a robust deep-learning tool that does not require advanced coding skills to analyze whole-brain imaging data in health and disease.
FörderungenInstitute for Tissue Engineering and Regenerative Medicine Institute of Molecular Biosciences Vascular Dementia Research Foundation Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy within the framework of the Munich Cluster for Systems Neurology DFG German Federal Ministry of Education and Research (Bundesministerium fr Bildung und Forschung) European Research Council Consolidator grant Nomis Heart Atlas Project Grant (Nomis Foundation) European Research Council under the European Union Edith-Haberland-Wagner Stiftung DFG through TUM International Graduate School of Science and Engineering
Deutsche Forschungsgemeinschaft (German Research Foundation)