基于多尺度特征提取与多特征融合的甲状腺结节超声影像分割OA

Objective To address the challenges of significant variations in nodule size,low image contrast,and strong speckle noise in ultrasound image segmentation of thyroid nodules,this paper proposes a precise segmentation method based on multi-scale feature extraction and global multi-feature fusion,aiming to enhance the recognition capability of features at different scales and improve generalization performance under complex backgrounds.Methods A classical encoder-decoder network architecture is adopted as the foundational framework.Building upon this,a Multi-scale Residual Connection Module(MRCM)is integrated to capture multi-scale nodule features that traditional fixed-receptive-field convolution kernels struggle to capture simultaneously.Concurrently,an Improved Global Pyramid Guidance(IGPG)module is introduced to achieve efficient and denoised global multi-feature fusion.The developed method was validated on two public datasets,TN3K and DDTI.Results The MRCM achieves continuous expansion of the receptive field by densely stacking three cascaded 3×3 convolution kernels,enabling it to adaptively capture features at different scales—from fine local textures to broader contextual information—effectively addressing drastic variations in nodule size.The IGPG module abandons dilated convolutions,instead utilizing MRCM for cross-layer feature fusion,and employs deep semantic information as a spatial attention guide to effectively filter and integrate multi-stage features,thereby significantly suppressing noise interference and enhancing the expression ability of discriminative features.On the TN3K dataset,the algorithm achieved IoU,HD95,and F1 scores of 72.21％,18.49,and 80.97％respectively.Compared to the U-Net baseline,IoU increased by 4.0％and F1 by 3.5％.On the DDTI dataset,the algorithm's IoU,HD95,and F1 scores were 63.40％,18.61,and 74.97％respectively.Compared to the U-Net baseline,IoU increased by 3.0％and F1 by 2.4％.The results significantly outperformed existing mainstream methods such as FCN,U-Net,MultiResUNet,SGUNet,and DC-UNet,and the effectiveness and synergistic effect of the MRCM and IGPG modules were demonstrated in ablation experiments.Conclusions By synergistically integrating the MRCM and IGPG modules,the proposed segmentation method effectively addresses the issues of scale variation and noise interference in ultrasound image segmentation of thyroid nodules,markedly improving segmentation accuracy and robustness,thereby offering a novel technical solution for computer-aided diagnosis systems of thyroid nodules.