Work

A morphology-spatial awareness network for neuronal classification and retrieval

Year: 2026

Type: article

Abstract: The study of neuronal morphology is vital for brain research. Traditional morphometric methods rely on global or handcrafted features, while current deep learning struggles to capture key multi-scale features and spatial structures, limiting representation of complex morphology. To address these challenges, we propose an end-to-end morphology-spatial awareness network (MSA-Net) based on point clouds. By representing 3D neuronal morphologies as point clouds, MSA-Net faithfully preserves their intrinsic geometric information. Specifically, it integrates a spatial-aware clustering (SAC) attention mechanism to adaptively capture discriminative features from critical morphological regions, and further proposes a global position-local feature interaction (GPLFI) module to facilitate the synergy between global positional embeddings and local point features, ultimately generating compact and robust low-dimensional representations for individual neurons. Extensive evaluations on two datasets demonstrate that MSA-Net exhibits strong discriminative capability in characterizing complex neuronal morphologies and consistently outperforms state-of-the-art methods in both classification and retrieval tasks, thereby establishing a robust paradigm for automated, large-scale neuronal morphology analysis. Our code is available at https://github.com/Jerry01902/MSA-Net .

Source: Journal of Innovative Optical Health Sciences

DOI: 10.1142/s1793545826500057