BFG&MSF-Net: Boundary Feature Guidance and Multi-Scale Fusion Network for Thyroid Nodule Segmentation
Jianuo Liu, Juncheng Mu, Haoran Sun, Chenxu Dai, Zhanlin Ji, Иван Ганчев
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, 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 layers 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-source 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 models, which makes it a promising solution for accurate thyroid nodule segmentation in ultrasound images.