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.

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.

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.

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.