The article introduces DELiVR, a virtual reality (VR)-aided deep-learning pipeline for detecting c-Fos+ cells in cleared mouse brains, which serve as markers for neuronal activity. DELiVR leverages VR for efficient annotation of training data, significantly improving the performance of cell-segmenting approaches. The pipeline is available as a user-friendly Docker container with a Fiji plugin, enabling data visualization and customization for other cell types. DELiVR was tested on cancer-related brain activity, revealing distinct activation patterns between weight-stable cancer and cancers associated with weight loss. The system outperforms threshold-based methods, achieving higher accuracy and sensitivity in cell detection. DELiVR also successfully segments microglia cells, demonstrating robust performance with an F1 score of 0.92. The study highlights the utility of VR in accelerating annotation and enhancing the accuracy of deep-learning models for whole-brain imaging analysis. DELiVR provides a comprehensive tool for analyzing neuronal activity in health and disease, with applications in understanding cancer-related brain activation patterns. The method is accessible to biologists without advanced coding skills, offering a versatile and efficient solution for 3D whole-brain data analysis.The article introduces DELiVR, a virtual reality (VR)-aided deep-learning pipeline for detecting c-Fos+ cells in cleared mouse brains, which serve as markers for neuronal activity. DELiVR leverages VR for efficient annotation of training data, significantly improving the performance of cell-segmenting approaches. The pipeline is available as a user-friendly Docker container with a Fiji plugin, enabling data visualization and customization for other cell types. DELiVR was tested on cancer-related brain activity, revealing distinct activation patterns between weight-stable cancer and cancers associated with weight loss. The system outperforms threshold-based methods, achieving higher accuracy and sensitivity in cell detection. DELiVR also successfully segments microglia cells, demonstrating robust performance with an F1 score of 0.92. The study highlights the utility of VR in accelerating annotation and enhancing the accuracy of deep-learning models for whole-brain imaging analysis. DELiVR provides a comprehensive tool for analyzing neuronal activity in health and disease, with applications in understanding cancer-related brain activation patterns. The method is accessible to biologists without advanced coding skills, offering a versatile and efficient solution for 3D whole-brain data analysis.