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CSRefiner: a lightweight framework for fine-tuning cell segmentation models with small datasets.

Can Shi^1,2,3, Yumei Li^1,2, Jing Guo^1,2

¹State Key Laboratory of Genome and Multi-omics Technologies, BGI Research, Shenzhen, 518083, China.

Briefings in Bioinformatics

|January 13, 2026

Summary

This summary is machine-generated.

CSRefiner enhances spatial omics by improving single-cell segmentation accuracy for whole-tissue analysis. This framework offers a lightweight solution for precise spatial gene expression data with minimal training data.

Keywords:

cell segmentation fine-tuning spatial omics

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Analysis of Multidimensional Microscopy Data Using Cell-ACDC

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Area of Science:

Genomics
Bioinformatics
Cell Biology

Background:

Spatial omics technologies enable transcriptome profiling at subcellular resolution.
Deep learning models offer high accuracy but struggle with whole-tissue analysis and diverse cell populations.
Current fine-tuning methods are often resource-intensive and lack adaptability.

Purpose of the Study:

To introduce CSRefiner, a novel fine-tuning framework for precise whole-tissue single-cell spatial expression analysis.
To address limitations in current deep learning-based cell segmentation for spatial transcriptomics.
To provide a scalable and adaptable solution for practical applications.

Main Methods:

Developed CSRefiner, a lightweight and efficient fine-tuning framework.
Integrated support for fine-tuning widely used segmentation models in spatial omics.
Evaluated performance across diverse staining types and multiple mainstream models.

Main Results:

CSRefiner achieves high accuracy in whole-tissue single-cell segmentation with limited annotated data.
Demonstrated superior performance across various staining types.
Showcased compatibility with multiple mainstream segmentation models.

Conclusions:

CSRefiner offers a practical and robust solution for real-world spatial transcriptomics.
The framework combines operational simplicity with high accuracy for precise spatial gene expression analysis.
Enables more reliable biological interpretation from subcellular resolution data.