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Updated: May 1, 2026

Analysis of Tubular Membrane Networks in Cardiac Myocytes from Atria and Ventricles

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Published on: October 15, 2014

TSSP-UNet: A Two-Stage Weakly Supervised Pathological Image Segmentation With Point Annotations.

Shaoqiang Wang¹, Guiling Shi¹, Yuchen Wang¹

¹Qingdao University of Technology, Qingdao, China.

IET Systems Biology

|March 3, 2026

Summary

This summary is machine-generated.

This study introduces TSSP-UNet, a novel two-stage weakly supervised segmentation method. It effectively improves cell nucleus segmentation accuracy using pseudo-labels and refined learning, outperforming baseline approaches.

Keywords:

image segmentation machine learning neural network

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Last Updated: May 1, 2026

Analysis of Tubular Membrane Networks in Cardiac Myocytes from Atria and Ventricles

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Evaporation-reducing Culture Condition Increases the Reproducibility of Multicellular Spheroid Formation in Microtiter Plates

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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

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Published on: July 5, 2024

Area of Science:

Medical image analysis
Computer vision
Machine learning

Background:

Deep convolutional neural networks excel at image segmentation but struggle with complex instances and annotation costs.
Weakly supervised learning offers a solution by utilizing less precise annotations or algorithm-derived supervision.

Purpose of the Study:

To develop an effective two-stage weakly supervised segmentation approach for complex image segmentation tasks.
To address the challenges of high-precision data annotation in medical imaging.

Main Methods:

Proposed TSSP-UNet, a two-stage segmentation network incorporating constraint and attention mechanisms on pseudo-labels.
Utilized boundary and superpixel information, along with contour enhancement via pseudo-labels and binary masks.
Employed a feature aggregation network for foreground segmentation and a confident learning algorithm for pseudo-label refinement.

Main Results:

TSSP-UNet demonstrated strong performance in weakly supervised cell nucleus segmentation.
The approach showed significant improvements compared to baseline methods on the MoNuSeg and TNBC datasets.

Conclusions:

The proposed TSSP-UNet effectively handles complex instances and reduces annotation dependency in image segmentation.
This method offers a promising solution for accurate cell nucleus segmentation in challenging datasets.