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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 …