Ultrasound thyroid nodule segmentation algorithm based on wavelet transform and CNN-Transformer

ZHENG Shuijing; YANG Jun; CAI Yujiao; WEN Jing

doi:10.16016/j.2097-0927.202409145

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Ultrasound thyroid nodule segmentation algorithm based on wavelet transform and CNN-Transformer

Monographic Reports | 更新时间：2025-07-25

- Ultrasound thyroid nodule segmentation algorithm based on wavelet transform and CNN-Transformer
- Journal of Army Medical University Vol. 47, Issue 14, Pages: 1595-1601(2025)
- 作者机构：
  
  1.重庆大学计算机学院模式识别实验室，重庆
  2.陆军军医大学（第三军医大学）第二附属医院普通外科，重庆
- 作者简介：
  
  WEN Jing， E-mail： wj@cqu.edu.cn
- 基金信息：
  
  the Joint Project of Chongqing Science and Technology Commission and Chongqing Health Commission(CSTB2023TIAD-KPX0050;2024ZDXM007);the Fundamental Research Fund for the Central Universities of China(2022CDJYGRH0156)
- DOI：10.16016/j.2097-0927.202409145
  CLC： R312;R445.1;R581.04
- Received：26 September 2024，
  
  Revised：02 November 2024，
  
  Published：30 July 2025
- 稿件说明：
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郑水婧，杨君，蔡瑜娇，等.基于小波变换和CNN-Transformer的超声甲状腺结节分割算法研究［J］.陆军军医大学学报，2025，47（14）：1595-1601.DOI：10.16016/j.2097-0927.202409145.

ZHENG S J，YANG J，CAI Y J，et al.Ultrasound thyroid nodule segmentation algorithm based on wavelet transform and CNN-Transformer［J］.J Army Med Univ，2025，47（14）：1595-1601.DOI：10.16016/j.2097-0927.202409145.
郑水婧，杨君，蔡瑜娇，等.基于小波变换和CNN-Transformer的超声甲状腺结节分割算法研究［J］.陆军军医大学学报，2025，47（14）：1595-1601.DOI：10.16016/j.2097-0927.202409145. DOI：

ZHENG S J，YANG J，CAI Y J，et al.Ultrasound thyroid nodule segmentation algorithm based on wavelet transform and CNN-Transformer［J］.J Army Med Univ，2025，47（14）：1595-1601.DOI：10.16016/j.2097-0927.202409145. DOI：

摘要

目的

融合小波变换和CNN-Transformer构建甲状腺结节自动分割网络，以提升甲状腺结节超声影像的智能分割效率和精准度。

方法

收集2023年5月至2024年2月在陆军军医大学第二附属医院超声科获取的1 371套甲状腺结节超声影像。经过预处理和标准化后，数据按照8∶1∶1的比例划分为训练集、验证集和测试集。以UNet为基础，将CNN与Swin-Transformer并联作为编码器，并在编码器与解码器之间插入小波变换模块，完成甲状腺结节分割网络的构建。使用准确率、IoU和Dice系数指标在收集的内部数据集上评估分割模型性能。

结果

本研究最终在收集的1 371套超声甲状腺结节上进行验证，平均Dice系数达到79.63%，IoU达到67.3%。相较于UNet，分割准确度提升1.02%。其甲状腺结节分割结果位置准确，边缘平滑。分割出的甲状腺结节相比于其他方法分割出的结节，在轮廓形态上与医师的手工分割结果吻合度更高。相较于UNet方法，本方法对结节的纹理学习更加充分，规避了结节容易错误分割为周围组织的情况。

结论

构建的基于小波变换和CNN-Transformer的分割模型精度优于UNet、Attention-UNet、UNetv2等UNet变种网络以及SAM Med2D分割大模型等先进分割方法，能有效应用于超声甲状腺结节的精确分割，提升医师的工作效率。

Abstract

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.

关键词

Keywords

references

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