Attention gate and dilation U-shaped network (GDUNet): an efficient breast ultrasound image segmentation network with multiscale information extraction.

Background: In recent years, computer-aided diagnosis (CAD) systems have played an important role in breast cancer screening and diagnosis. The image segmentation task is the key step in a CAD system for the rapid identification of lesions. Therefore, an efficient breast image segmentation network is necessary for improving the diagnostic accuracy in breast cancer screening. However, due to the characteristics of blurred boundaries, low contrast, and speckle noise in breast ultrasound images, breast lesion segmentation is challenging. In addition, many of the proposed breast tumor segmentation networks are too complex to be applied in practice. Methods: We developed the attention gate and dilation U-shaped network (GDUNet), a lightweight, breast lesion segmentation model. This model improves the inverted bottleneck, integrating it with tokenized multilayer perceptron (MLP) to construct the encoder. Additionally, we introduce the lightweight attention gate (AG) within the skip connection, which effectively filters noise in low-level semantic information across spatial and channel dimensions, thus attenuating irrelevant features. To further improve performance, we innovated the AG dilation (AGDT) block and embedded it between the encoder and decoder in order to capture critical multiscale contextual information. Results: We conducted experiments on two breast cancer datasets. The experiment's results show that compared to UNet, GDUNet could reduce the number of parameters by 10 times and the computational complexity by 58 times while providing a double of the inference speed. Moreover, the GDUNet achieved a better segmentation performance than did the state-of-the-art medical image segmentation architecture. Conclusions: Our proposed GDUNet method can achieve advanced segmentation performance on different breast ultrasound image datasets with high efficiency.