SAFM-Net: a dual-branch CNN–GNN network with synergistic attention and frequency-domain modulation for ultrasound thyroid nodule segmentation

Abstract

Accurate segmentation of thyroid nodules in ultrasound images is essential for thyroid cancer risk assessment and computer-aided diagnosis, yet remains challenging due to ambiguous boundaries and significant shape variations. Existing convolutional neural network (CNN)-based methods effectively capture local features but are limited in modeling long-range dependencies and complex boundary structures. To address these limitations, we propose SAFM-Net, a dual-branch CNN–GNN Network that integrates synergistic attention and frequency-domain modulation. The network adopts a dual-branch encoder, where a graph-based branch leverages a synergistic-attention dynamic graph convolution (SA-DGC) module to adaptively model global relationships among feature nodes, enhancing structural and boundary representation. In parallel, a CNN branch captures local textures and fine-grained details. To fuse complementary features, a frequency-domain modulation (FDM) module is introduced to enable cross-branch interaction and hierarchical integration, improving feature representation capability. Extensive experiments on the DDTI and TN3K datasets demonstrate the effectiveness of the proposed method. Compared with GED-Net, SAFM-Net achieves improvements of 0.54%, 1.00%, 1.68%, and 0.73% in terms of Accuracy, Dice, IoU, and Precision, respectively, on the DDTI dataset, and improvements of 0.17%, 0.43%, 0.67%, and 1.48% in terms of Accuracy, Dice, IoU, and Precision, respectively, on the TN3K dataset. These results indicate that SAFM-Net provides accurate and robust segmentation performance under challenging ultrasound imaging conditions.