MHAHF-UNet: a multi-scale hybrid attention hierarchy fusion network for carotid artery segmentation

Carotid plaque is an early manifestation of carotid atherosclerosis, and its accurate segmentation helps to assess cardiovascular disease risk. However, existing carotid artery segmentation algorithms are difficult to accurately capture the structural features of morphologically diverse plaques and lack effective utilization of multilayer features.

In order to solve the above problems, this paper proposes a multi-scale hybrid attention hierarchical fusion U-network structure (MHAHF-UNet) for segmenting ambiguous plaques in carotid artery images in order to improve the segmentation accuracy for complex structured images. The structure firstly introduces the median-enhanced orthogonal convolution module (MEOConv), which not only effectively suppresses the noise interference in ultrasound images, but also maintains the ability to perceive multi-scale features by combining the median-enhanced ternary channel mechanism and the depth-orthogonal convolution space mechanism. Secondly, it adopts the multi-fusion group convolutional gating module, which realizes the effective integration of shallow detailed features and deep semantic features through the adaptive control strategy of group convolution, and is able to flexibly regulate the transfer weights of features at different levels.

Experiments show that the MHAHF-UNet model achieves a Dice coefficient of $82.46\pm 0.31\%$ and an IOU of $71.45\pm 0.37\%$ in the carotid artery segmentation task.

The model is expected to provide strong support for the prevention and treatment of cardiovascular diseases.