Self-supervised learning analysis of multi-FISH labeled cell-type map in thick brain slices.

ABSTRACT:

Introduction

Accurate mapping of the spatial distribution of diverse cell types is essential for understanding the cellular organization of brain. However, the cellular heterogeneity and the substantial cost of manual annotation of cells in volumetric images hinder existing neural networks from achieving high-precision segmentation of multiple cell-types within a unified framework.

Methods

To address this challenge, we introduce a self-supervised learning framework, Voxelwise U-shaped Swin-Mamba network (VUSMamba), for automatic segmentation of multiple neuronal populations in 300 μm thick brain slices. VUSMamba employs contrastive learning and pretext tasks for self-supervised learning on unlabeled data, followed by fine-tuning with minimal annotations. As a proof of concep

SUBMITTER: Zheng W 

PROVIDER: S-EPMC12277362 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature