Objective To develop an automatic segmentation network for thyroid nodules by integrating wavelet transform and CNN-Transformer in order to improve the efficiency and accuracy of ultrasound image segmentation.Methods A total of 1 371 sets of ultrasound images of thyroid nodules were collected from Department of Ultrasonography of Second Affiliated Hospital of Army Medical University between May 2023 and February 2024.After preprocessing and normalization,the data were divided into training,validation,and testing sets in a ratio of 8∶1∶1.Based on UNet,CNN and Swin-Transformer were used in parallel as the encoder,with a wavelet transform module inserted between the encoder and decoder to construct a thyroid nodule segmentation network.The performance of the segmentation model was evaluated on the collected internal dataset using accuracy,IoU,and Dice coefficient metrics.Results The finally verified 1 371 sets of ultrasonic thyroid nodules had an average Dice coefficient of 79.63%and an IoU of 67.30%.Compared with UNet,the segmentation accuracy was improved by 1.02%.The segmentation model obtained accurate location and smooth edges of thyroid nodules,and the segmentation was more consistent in thyroid nodule edge and morphology with those marked by doctors manually when compared with other segmentations.Compared with UNet,this segmentation method can learn the texture of nodules more fully and avoid the situation that nodules had been incorrectly divided into surrounding tissues.Conclusion Our developed segmentation model based on wavelet transform and CNN-Transformer demonstrates better segmentation accuracy in comparison to conventional UNet variants,such as UNet,Attention-UNet,and UNetv2,and medical segment anything models like SAM Med2D.This segmentation method enables accurate segmentation of ultrasound thyroid nodules,thereby enhancing clinical workflow efficiency through automated precise delineation.

ObjectiveTo explore the correlation between angiotensin converting enzyme (ACE) gene polymorphism and hypertension accompanying atherosclerosis in Li people in Hainan province. MethodsTwo hundred and sixty patients with hypertension accompanying atherosclerosis were selected as hypertension plus atherosclerosis group, while two hundred and seventy-six healthy people were regarded as healthy control group. ACE I/D gene polymorphism was detected by polymerase chain reaction (PCR), and the genotype frequencies and allele frequencies of DD, DI and Ⅱ were investigated. The carotid intimal-medial thickness(IMT)was measured by high-resolution ultrasound technique and mean IMT (MIMT) was calculated. Results(1) In the hypertension plus atherosclerosis group, the genotype frequencies of DD, DI and Ⅱ were 15.0%, 37.3%, 47.7%,respectively, and the allele frequencies of D and I were 33.70% and 66.30%, respectively. In the healthy control group, the genotype frequencies of DD, DI and Ⅱ were 17.8% , 40.6% and 41.7%,respectively, and the allele frequencies of D and I were 38.0% and 62.0%, respectively. There were no significant differences both in the genotype frequencies of DD, DI and Ⅱ, and in allele frequencies of D and I between the two groups (P>0. 05). (2) The age,total serum cholesterol(TC),triglyceride (TG), systolic pressure(SBP), diastolic pressure(DBP), apolipoprotein A(apoA) and apolipoprotein B (apoB) levels were significantly higher in the hypertension plus atherosclerosis group than in the control group(P<0. 05). The high density lipoprotein cholesterol(HDL-C) level was significantly lower in the hypertension plus atherosclerosis group than in the control group(P<0. 05). Logistic regression analysis showed that TG (OR = 2.14), apoA(OR = 360. 39), SBP(OR = 1.21), DBP (OR=1.08) and ACE DD genetype (OR = 0. 30) had correlation with hypertension plus atherosclerosis(all P<0. 05). The MIMT level was significantly higher in ACE DD subset than in DI and Ⅱ subset (P<0.05). ConclusionsThe ACE DD genotype increases the susceptibility of carotid atheroselerosis, which is the risk factor for hypertension accompanying atherosclerosis in Li people in Hainan province. It may be an early predictive factor in atherosclerosis.