Abstract

Info

Title:: Thyroid nodules segmentation in ultrasound image based on multi-scale feature extraction and multi-feature fusion

Author(s):: CONG Peilu1; ZHANG Chong1; YUN Kai1; ZHAO Shuang2; ZHAO Wenhua1; MA Zhiqing1; 1. School of Medical Information Engineering, Shandong University of Traditional Chinese Medicine, Jinan 250355, China 2. Experimental Center, Shandong University of Traditional Chinese Medicine, Jinan 250355, China

Keywords:: Keywords: thyroid nodule ultrasound image segmentation multi-scale feature extraction attention

Abstract:: Abstract: Based on the classical U-Net, a novel method incorporating multi-scale feature extraction and multi-feature fusion for thyroid nodule segmentation in ultrasound image is proposed. Specifically, a feature extraction strategy based on stacked small-sized convolutional kernels is designed. By stacking multiple small-sized convolutional kernels, the model can capture both detailed and global features of images under different receptive fields, thereby achieving efficient multi-scale feature extraction. Through a hybrid attention mechanism which includes both channel and spatial attention, feature maps from different stages are effectively fused, thereby enhancing the original skip connections. The proposed algorithm achieves 95% Hausdorff distance (HD95) of 16.02 and 17.86 mm on the TN3K and DDTI thyroid nodule segmentation datasets, respectively, along with F1-scores of 82.21% and 75.74%, outperforming all other compared methods. Experimental results demonstrate that this approach can provide valuable assistance to clinicians in diagnostic practice.