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A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment

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DRSeg: a weakly supervised framework for breast ultrasound image segmentation.

Meng Xu¹, Bin Hu¹, Yingfeng Wang²

¹Department of Computer Science and Technology, Kean University, Union, 07083, USA.

Scientific Reports

|April 28, 2026

Summary

This study introduces DRSeg, a weakly supervised segmentation framework for breast ultrasound images. DRSeg improves tumor segmentation accuracy by effectively managing pseudo-label quality, reducing the need for extensive pixel-level annotations.

Keywords:

Breast ultrasound Image selection Pseudo-label generation Weakly supervised segmentation

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Area of Science:

Medical Imaging
Artificial Intelligence
Computer Vision

Background:

Accurate breast tumor segmentation in ultrasound images aids clinical decisions.
Fully supervised methods demand extensive pixel-level annotations, which are labor-intensive.
Weakly supervised segmentation using image-level labels is a viable alternative, but pseudo-label quality is a hurdle.

Purpose of the Study:

To propose DRSeg, a weakly supervised segmentation framework for breast ultrasound images.
To address the challenge of variable pseudo-label quality in automated segmentation.
To reduce the dependency on pixel-wise annotations while maintaining high segmentation performance.

Main Methods:

DRSeg employs a Class Activation Map (CAM)-based pipeline with refinements.
A Dual-Region of Interest (ROI) selection algorithm identifies stable CAM localizations for reliable pseudo-label generation.
Pseudo-labels are generated using the Segment Anything Model, and a Mean Teacher strategy trains the segmentation model.

Main Results:

DRSeg demonstrated effectiveness on the BUSI and BLU datasets.
With Swin Transformer V2, DRSeg achieved 59.35% IoU and 69.27% F1 score on BUSI.
With ResNet-50, DRSeg achieved 51.79% IoU and 60.00% F1 score on BLU, outperforming existing methods.

Conclusions:

DRSeg effectively handles pseudo-label quality variations in weakly supervised breast ultrasound segmentation.
The framework achieves competitive segmentation performance, comparable to fully supervised methods.
DRSeg offers a practical solution for automated breast tumor segmentation with reduced annotation burden.