Frequency-Phase Guided Attention Complex-Valued Network for Ultrasound Image Segmentation

Ultrasound imaging has emerged as an effective tool for aiding diagnosis. The automatic segmentation of ultrasound images is crucial in identifying the lesion target and evaluating clinical indicators for accurate diagnosis and prognosis. However, the segmentation problems are challenging due to the inherent speckle noise interference and low contrast of ultrasound images. The complex-value-based neural network can directly deal with the phase components, offering a potential solution in a better-perceiving structure for ultrasound image segmentation. In this study, we develop a Frequency Phase-Guided Attention Network (FPGANet) for ultrasound image segmentation by exploring the properties of the complex-valued model under the guide of phase and frequency perspectives. First, our proposed method transforms images into a complex domain as the input to an advanced complex-value model consisting of pure complex-value convolutions and operations. Especially this model can then effectively scrutinize phase information to distinguish target areas from similar backgrounds better. Moreover, we introduce a complex hybrid attention module following complex convolution to selectively adjust the perception of phase components and the model's bias. Also, we designed a frequency-adaptive separation module to emphasize frequency features prioritized by the encoder and decoder using a combination of wavelet decomposition and frequency channel attention. We evaluate the proposed FPGANet on three publicly available ultrasound datasets of breast, cardiac and thyroid nodules and a private abdominal effusion ultrasound dataset. Comparative experiments were also conducted with state-of-the-art methods. The results demonstrate the superior performance of FPGANet, implying its potential for advancing ultrasound image segmentation.