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Persistent homology-based segmentation tool for membrane images.

Haruhisa Oda¹, Yusuke Imoto²

¹Department of Preventive Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.

Cell Reports Methods

|March 27, 2026

Summary

This summary is machine-generated.

PomSeg, a new membrane segmentation tool, uses persistent homology for intuitive parameter tuning and robust cell shape analysis. This method offers flexibility and interpretability for various imaging applications.

Keywords:

CP: computational biology CP: imaging embryo membrane image persistent homology segmentation topological data analysis tracking

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Area of Science:

Computational Biology
Image Analysis
Topological Data Analysis

Background:

Automated cell segmentation using machine learning often presents challenges in parameter tuning for users.
Existing methods may lack interpretability and control over the segmentation process.

Purpose of the Study:

To develop PomSeg, a novel membrane segmentation method utilizing persistent homology.
To enable intuitive and efficient parameter tuning by linking parameters to cell shape information.
To demonstrate the robustness and versatility of PomSeg across different image types.

Main Methods:

Development of PomSeg, a segmentation method based on persistent homology.
Leveraging topological features captured by persistent homology for parameter interpretation.
Testing PomSeg on early mouse embryo membrane images and other public datasets.

Main Results:

PomSeg demonstrated intuitive and efficient parameter tuning due to its connection to cell shape.
The method showed robustness and stability when applied to various image types, including coarse-resolution data.
Validation through application to mouse embryo images and public datasets confirmed flexibility, versatility, and agreement with ground truth.

Conclusions:

PomSeg offers a valuable tool for researchers seeking control and interpretability in cell segmentation.
The method's adaptivity and stability make it suitable for diverse imaging challenges.
Potential for extension by integrating machine learning tools, indicating broader applicability.