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

## TL;DR

This paper introduces a new model for breast ultrasound image segmentation that improves accuracy by combining Mamba-CNN with feature interaction techniques.

## Contribution

The novel integration of Mamba-CNN and a hybrid attention enhancement mechanism improves segmentation performance in noisy ultrasound images.

## Key findings

- The proposed model achieved a Dice similarity coefficient of 76.04% on BUSI and UDIAT datasets.
- The HD95 index reached 20.28 mm, showing improved noise and artifact handling.
- The model outperformed existing algorithms in segmentation accuracy.

## Abstract

The large scale and shape variation in breast lesions make their segmentation extremely challenging. A breast ultrasound image segmentation model integrating Mamba-CNN and feature interaction is proposed for breast ultrasound images with a large amount of speckle noise and multiple artifacts. The model first uses the visual state space model (VSS) as an encoder for feature extraction to better capture its long-range dependencies. Second, a hybrid attention enhancement mechanism (HAEM) is designed at the bottleneck between the encoder and the decoder to provide fine-grained control of the feature map in both the channel and spatial dimensions, so that the network captures key features and regions more comprehensively. The decoder uses transposed convolution to upsample the feature map, gradually increasing the resolution and recovering its spatial information. Finally, the cross-fusion module (CFM) is constructed to simultaneously focus on the spatial information of the shallow feature map as well as the deep semantic information, which effectively reduces the interference of noise and artifacts. Experiments are carried out on BUSI and UDIAT datasets, and the Dice similarity coefficient and HD95 indexes reach 76.04% and 20.28 mm, respectively, which show that the algorithm can effectively solve the problems of noise and artifacts in ultrasound image segmentation, and the segmentation performance is improved compared with the existing algorithms.