Ultrasound thyroid nodule segmentation algorithm based on wavelet transform and CNN-Transformer
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 f...
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| Language: | zho |
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Editorial Office of Journal of Army Medical University
2025-07-01
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| Series: | 陆军军医大学学报 |
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| Online Access: | https://aammt.tmmu.edu.cn/html/202409145.html |
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| author | ZHENG Shuijing YANG Jun CAI Yujiao |
| author_facet | ZHENG Shuijing YANG Jun CAI Yujiao |
| author_sort | ZHENG Shuijing |
| collection | DOAJ |
| description | 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.
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| format | Article |
| id | doaj-art-ac9ec4a3aa4e4bc7a3c5a4422a597b37 |
| institution | DOAJ |
| issn | 2097-0927 |
| language | zho |
| publishDate | 2025-07-01 |
| publisher | Editorial Office of Journal of Army Medical University |
| record_format | Article |
| series | 陆军军医大学学报 |
| spelling | doaj-art-ac9ec4a3aa4e4bc7a3c5a4422a597b372025-08-20T02:45:41ZzhoEditorial Office of Journal of Army Medical University陆军军医大学学报2097-09272025-07-0147141595160110.16016/j.2097-0927.202409145Ultrasound thyroid nodule segmentation algorithm based on wavelet transform and CNN-TransformerZHENG Shuijing0YANG Jun1CAI Yujiao2 Laboratory of Pattern Recognition， College of Computer Science， Chongqing University， ChongqingDepartment of General surgery， Second Affiliated Hospital， Army Medical University （Third Military Medical University）， Chongqing， ChinaDepartment of General surgery， Second Affiliated Hospital， Army Medical University （Third Military Medical University）， Chongqing， ChinaObjective 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. https://aammt.tmmu.edu.cn/html/202409145.htmlthyroid nodule segmentationwavelet transformultrasound image diagnosisdeep learning |
| spellingShingle | ZHENG Shuijing YANG Jun CAI Yujiao Ultrasound thyroid nodule segmentation algorithm based on wavelet transform and CNN-Transformer 陆军军医大学学报 thyroid nodule segmentation wavelet transform ultrasound image diagnosis deep learning |
| title | Ultrasound thyroid nodule segmentation algorithm based on wavelet transform and CNN-Transformer |
| title_full | Ultrasound thyroid nodule segmentation algorithm based on wavelet transform and CNN-Transformer |
| title_fullStr | Ultrasound thyroid nodule segmentation algorithm based on wavelet transform and CNN-Transformer |
| title_full_unstemmed | Ultrasound thyroid nodule segmentation algorithm based on wavelet transform and CNN-Transformer |
| title_short | Ultrasound thyroid nodule segmentation algorithm based on wavelet transform and CNN-Transformer |
| title_sort | ultrasound thyroid nodule segmentation algorithm based on wavelet transform and cnn transformer |
| topic | thyroid nodule segmentation wavelet transform ultrasound image diagnosis deep learning |
| url | https://aammt.tmmu.edu.cn/html/202409145.html |
| work_keys_str_mv | AT zhengshuijing ultrasoundthyroidnodulesegmentationalgorithmbasedonwavelettransformandcnntransformer AT yangjun ultrasoundthyroidnodulesegmentationalgorithmbasedonwavelettransformandcnntransformer AT caiyujiao ultrasoundthyroidnodulesegmentationalgorithmbasedonwavelettransformandcnntransformer |