GMS-UHRNet: a global multi-scale spatial U-HRNet for breast ultrasound image segmentation

Automated breast ultrasound image segmentation enhances radiologists' ability to enhance breast cancer diagnosis accuracy. In view of the inherent speckle artifacts, fuzzy breast lesion boundaries, and inhomogeneity within breast lesions in ultrasound images, this paper proposes a multiscale residual UHRNet network based on a global multiscale attention mechanism (GMS-UHRNet) to enhance the segmentation accuracy of breast lesions in ultrasound images. On the basic framework of UHRNet, the feature expression ability is effectively improved by parallel multiscale residual fusion approach, which enhances the segmentation accuracy and robustness of the network. In the GMS-UHRNet network, a global multiscale attention mechanism is added, which successfully reduces the semantic difference with the encoder and enables the network to recognize breast lesion regions more accurately. Extensive experiments on the BUSI dataset demonstrate the network's superior performance in breast ultrasound lesion segmentation compared to other medical image segmentation methods.