Virtual reality-empowered deep-learning analysis of brain cells

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.

Doris Kaltenecker, Rami Al-Maskari, Moritz Negwer, Luciano Hoeher, Florian Kofler, Shan Zhao, Mihail Todorov, Zhouyi Rong, Johannes Christian Paetzold, Benedikt Wiestler, Marie Piraud, Daniel Rueckert, Julia Geppert, Pauline Morigny, Maria Rohm, Bjoern H. Menze, Stephan Herzig, Mauricio Berriel Diaz, Ali Ertürk