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Skeleton-guided sparse anchors for rotated instance segmentation in cell microscopy.

Jun Wang¹, Chengfeng Zhou², Zhaoyan Ming¹

¹School of Computer and Computing Science, Hangzhou City University, Hangzhou, 310015, China.

Computer Methods and Programs in Biomedicine

|April 8, 2026

Summary

This summary is machine-generated.

Accurate cell segmentation in microscopy is challenging. A novel framework, A2B-IS (Attention-to-Box Instance Segmentation), improves instance segmentation of clustered cells using pixel masks and rotated bounding boxes, enhancing analysis of densely packed cell populations.

Keywords:

Attention Instance segmentation Microscopy Semi-supervised learning

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Area of Science:

Computational biology
Image analysis
Machine learning

Background:

Accurate instance segmentation of clustered cells in microscopy is a significant challenge.
Traditional methods struggle with overlapping or touching cells of varying sizes and shapes.

Purpose of the Study:

To introduce A2B-IS (Attention-to-Box Instance Segmentation), a novel one-stage framework for improved cell segmentation.
To enhance segmentation quality in densely packed cell regions using pixel-level masks and rotated bounding boxes.

Main Methods:

A2B-IS employs parallel mask and box prediction branches to simplify the pipeline.
A Gaussian skeleton map guides anchor placement and corrects box predictions.
An Atrous Attention Block enriches feature representations with multiscale details.
Semi-supervised learning is utilized to improve model robustness and generalization.

Main Results:

A2B-IS outperformed leading one- and two-stage segmentation methods on large-scale microscopy datasets.
The framework achieved higher average precision and recall, especially in dense clusters and for small/irregular cells.

Conclusions:

A2B-IS offers substantial improvements for challenging microscopy segmentation tasks.
The combination of techniques in A2B-IS enables more reliable automated analysis of cell populations.