德国慕尼黑大学Ali Ertürk等研究人员合作实现虚拟现实驱动的脑细胞深度学习分析。2024年4月22日，《自然—方法学》杂志在线发表了这项成果。

据介绍，在全脑光片图像堆栈等三维数据集中自动检测特定细胞具有挑战性。

附：英文原文

Title: Virtual reality-empowered deep-learning analysis of brain cells

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

Issue&Volume: 2024-04-22

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.

DOI: 10.1038/s41592-024-02245-2

Source: https://www.nature.com/articles/s41592-024-02245-2