Brain-wide 3D neuron detection and mapping with deep learning.

Abstract

Significance: Mapping the spatial distribution of specific neurons across the entire brain is essential for understanding the neural circuits associated with various brain functions, which in turn requires automated and reliable neuron detection and mapping techniques.

Aim: To accurately identify somatic regions from 3D imaging data and generate reliable soma locations for mapping to diverse brain regions, we introduce NeuronMapper, a brain-wide 3D neuron detection and mapping approach that leverages the power of deep learning.

Approach: NeuronMapper is implemented as a four-stage framework encompassing preprocessing, classification, detection, and mapping. Initially, whole-brain imaging data is divided into 3D sub-blocks during the preprocessing phase. A lightweight classification network then identifies the sub-blocks containing somata. Following this, a Video Swin Transformer-based segmentation network delineates the soma regions within the identified sub-blocks. Last, the locations of the somata are extracted and registered with the Allen Brain Atlas for comprehensive whole-brain neuron mapping.

Results: Through the accurate detection and localization of somata, we achieved the mapping of somata at the one million level within the mouse brain. Comparative analyses with other soma detection techniques demonstrated that our method exhibits remarkably superior performance for whole-brain 3D soma detection.

Conclusions: Our approach has demonstrated its effectiveness in detecting and mapping somata within whole-brain imaging data. This method can serve as a computational tool to facilitate a deeper understanding of the brain's complex networks and functions.