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Breast Ultrasound Image Segmentation Integrating Mamba-CNN and Feature Interaction.

Guoliang Yang¹, Yuyu Zhang¹, Hao Yang¹

¹School of Electrical Engineering and Automation, Jiangxi University of Science and Technology, Ganzhou 341000, China.

Sensors (Basel, Switzerland)

|January 10, 2026

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This study introduces a novel Mamba-CNN model for segmenting breast ultrasound images, effectively reducing noise and artifacts. The enhanced model improves segmentation accuracy for challenging breast lesions.

Area of Science:

Medical Imaging
Artificial Intelligence
Biomedical Engineering

Background:

Breast lesion segmentation in ultrasound images is challenging due to variations in size, shape, noise, and artifacts.
Accurate segmentation is crucial for diagnosis and treatment planning.

Purpose of the Study:

To develop an advanced breast ultrasound image segmentation model.
To address challenges posed by speckle noise, artifacts, and lesion variability.
To improve the accuracy and robustness of breast lesion segmentation.

Main Methods:

A novel Mamba-CNN model integrating visual state space model (VSS) for feature extraction and a hybrid attention enhancement mechanism (HAEM).
A decoder utilizing transposed convolution for feature map upsampling and spatial information recovery.

Keywords:

breast ultrasound images segmentation cross fusion module hybrid attention enhancement mechanism mamba

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A cross-fusion module (CFM) to integrate shallow spatial and deep semantic information, mitigating noise and artifacts.

Main Results:

The model achieved a Dice similarity coefficient of 76.04% and an HD95 of 20.28 mm on the BUSI and UDIAT datasets.
Demonstrated significant improvement in segmentation performance compared to existing algorithms.
Effectively reduced the interference of noise and artifacts in ultrasound image segmentation.

Conclusions:

The proposed Mamba-CNN model offers an effective solution for segmenting noisy and artifact-laden breast ultrasound images.
The integration of VSS, HAEM, and CFM enhances the model's ability to capture long-range dependencies and refine feature representation.
The study highlights the potential of advanced deep learning architectures for improving diagnostic accuracy in breast imaging.