Virtual reality-empowered deep-learning analysis of brain cells

Nat Methods. 2024 Jul;21(7):1306-1315. doi: 10.1038/s41592-024-02245-2. Epub 2024 Apr 22.

Abstract

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.

MeSH terms

  • Animals
  • Brain* / diagnostic imaging
  • Deep Learning*
  • Humans
  • Image Processing, Computer-Assisted / methods
  • Mice
  • Neurons
  • Proto-Oncogene Proteins c-fos / metabolism
  • Software
  • Virtual Reality*

Substances

  • Proto-Oncogene Proteins c-fos