Objective:To propose a thyroid nodule ultrasound image segmentation method based on a feature-adaptive extraction and gated fusion mechanisms net (FAGF-Net).Methods:The FAGF-Net was constructed by designing a feature coupling encoder (FC-Encoder), which integrated a convolutional neural network and a Transformer to extract both local and global features. The perception of multi-scale geometric characteristics of nodules was enhanced by developing a multi-scale feature space coupling module (MFSC-Module), and a context-gated feature attention module (CGFA-Module) was introduced to filter redundant information and suppress noise interference. A retrospective analysis was conducted on 3 493 physician-annotated two-dimensional thyroid nodule ultrasound images obtained from the publicly available TN3K medical imaging dataset. The dataset was divided into training and validation sets at a ratio of 8∶2, resulting in 2 794 training images and 699 validation images. FAGF-Net was compared with several mainstream semantic segmentation models, including UNet, Deeplabv3, HRNet, PSPNet, and SegFormer using UNet as the baseline model. Additionally, module ablation experiments were performed to evaluate the effectiveness of each core module within the FAGF-Net. Visual comparisons were also conducted between the original ultrasound images, the annotated nodule regions, and the segmentation results produced by the different models.Results:The accuracy, intersection over union and Dice coefficient of the FAGF-Net method were 95.59%, 83.53% and 90.60%, respectively. These values were 3.77%, 2.29% and 3.55% higher than those of the UNet method (91.82%, 81.24% and 87.05%). Additionally, the FAGF-Net method achieved a frame rate of 19.8, meeting the requirements for image segmentation in medical environment scenarios. The module ablation experiments showed that compared with the UNet, the accuracy (92.85%), intersection over union (81.76%) and Dice coefficient (88.45%) of the FC-Encoder model were increased by 1.03%, 0.52% and 1.40%, respectively. The accuracy (93.13%), intersection over union (81.91%) and Dice coefficient (88.76%) of the CGFA-Module model were introduced separately, which were 1.31%, 0.67% and 1.71% higher than those of UNet, respectively. Compared with the FC-Encoder model alone, the accuracy (94.61%), intersection over union (82.45%) and Dice coefficient (89.54%) of the MFSC-Module increased by 1.76%, 0.69% and 1.09%, respectively when FC-Encoder was used as the feature double branch extraction encoder. Compared with the FC-Encoder model alone, the accuracy (94.39%), intersection over union (82.33%) and Dice coefficient (89.46%) of the CGFA-Module were increased by 1.54%, 0.57% and 1.01%, respectively, when FC-Encoder was used as the feature double branch extraction encoder. Visualization results showed that the FAGF-Net method produced smoother and more complete in boundary fitting and was more accurate at identifying small and low-contrast lesions.Conclusions:A thyroid nodule ultrasound image segmentation method based on FAGF-Net was proposed, which effectively improves the segmentation accuracy of thyroid nodule ultrasound image segmentation.

The prevalence rate of nonalcoholic fatty liver disease (NAFLD) is increasing year by year, and at present, it has become one of the most common chronic liver diseases in adults in China. NAFLD can progress from nonalcoholic fatty degeneration of the liver to nonalcoholic steatohepatitis, liver cirrhosis, hepatocellular carcinoma, NAFLD-associated cardiovascular events, and death. This article reviews the risk factors for the development of NAFLD and the progression of NAFLD to major diseases such as liver fibrosis, liver cirrhosis, liver cancer, related cardiovascular events, and death, in order to further explore the mechanism of the development and progression of NAFLD, reduce the prevalence rate of NAFLD, slow down the progression of NAFLD, reduce the mortality rate of related diseases, and achieve better prevention and treatment.

Childhood nonalcoholic fatty liver disease (NAFLD) is one of the most common cause of chronic liver diseases in children and adolescents; its unique histopathological and clinical features may lead to its progression to liver fibrosis, liver cirrhosis, and liver cancer, and compared with adult NAFLD, it is more likely to cause other diseases and increase mortality rate. Therefore, early identification of risk factors for childhood NAFLD, effective screening of high-risk population, active prevention, and early diagnosis and treatment are key to effective clinical management of this disease. This article elaborates on the risk factors, screening methods, and preventive healthcare measures for childhood NAFLD, in order to standardize the comprehensive management of NAFLD, reduce the prevalence rate of NAFLD, delay its progression, and alleviate the economic and public health burden brought by the disease.