BFG&MSF-Net: Boundary Feature Guidance and Multi-Scale Fusion Network for Thyroid Nodule Segmentation

Abstract

Accurately segmenting thyroid nodules in ultrasound images is crucial for computer-aided diagnosis. Despite the success of Convolutional Neural Networks (CNNs) and Transformers in natural images processing, they struggle with precise boundaries and small-object segmentation in ultrasound images. To address this, a novel BFG&MSF-Net model is proposed in this paper, utilizing four newly designed modules: (1) a Boundary Feature Guidance Module (BFGM) for improving the edge details capturing; (2) a Multi-Scale Perception Fusion Module (MSPFM) for enhancing the information capture by combining a novel Positional Blended Attention (PBA) with the Pyramid Squeeze Attention (PSA); (3) a Depthwise Separable Atrous Spatial Pyramid Pooling Module (DSASPPM), used in the bottleneck to improve the contextual information capturing; and (4) a Refinement Module (RM) optimizing the low-level features for better organ and boundary identification. Evaluated on the TN3K and DDTI open-access datasets, BFG&MSF-Net demonstrates effective reduction of boundary segmentation errors and superior segmentation performance compared to commonly used segmentation models and state-of-the-art models, which makes it a promising solution for accurate thyroid nodule segmentation in ultrasound images.

Keywords

• Ultrasound image
• thyroid nodule
• segmentation
• deep learning
• boundary feature guidance
• multi-scale fusion