Lu Chenzhuo, Fu Zhuang, Fei Jian, Xie Rongli, Lu Chenyue
School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China.
State Key Laboratory of Mechanical System and Vibration, Shanghai 200240, People's Republic of China.
Phys Med Biol. 2025 Jan 21;70(2). doi: 10.1088/1361-6560/ada5a6.
Ultrasound is the predominant modality in medical practice for evaluating thyroid nodules. Currently, diagnosis is typically based on textural information. This study aims to develop an automated texture classification approach to aid physicians in interpreting ultrasound images of thyroid nodules. However, there is currently a scarcity of pixel-level labeled datasets for the texture classes of thyroid nodules. The labeling of such datasets relies on professional and experienced doctors, requiring a significant amount of manpower. Therefore, the objective of this study is to develop an unsupervised method for classifying nodule textures.Firstly, we develop a spatial mapping network to transform the one-dimensional pixel value space into a high-dimensional space to extract comprehensive feature information. Subsequently, we outline the principles of feature selection that are suitable for clustering. Then we propose a pixel-level clustering algorithm with a region growth pattern, and a distance evaluation method for texture sets among different nodules is established.Our algorithm achieves a pixel-level classification accuracy of 0.931 for the cystic and solid region, 0.870 for the hypoechoic region, 0.959 for the isoechoic region, and 0.961 for the hyperechoic region. The efficacy of our algorithm and its concordance with human observation have been demonstrated. Furthermore, we conduct calculations and visualize the distribution of different textures in benign and malignant nodules.This method can be used for the automatic generation of pixel-level labels of thyroid nodule texture, aiding in the construction of texture datasets, and offering image analysis information for medical professionals.
超声是医学实践中评估甲状腺结节的主要方式。目前，诊断通常基于纹理信息。本研究旨在开发一种自动纹理分类方法，以帮助医生解读甲状腺结节的超声图像。然而，目前缺乏用于甲状腺结节纹理类别的像素级标注数据集。此类数据集的标注依赖于专业且经验丰富的医生，需要大量人力。因此，本研究的目的是开发一种用于结节纹理分类的无监督方法。首先，我们开发了一个空间映射网络，将一维像素值空间转换为高维空间，以提取综合特征信息。随后，我们概述了适用于聚类的特征选择原则。然后我们提出了一种具有区域生长模式的像素级聚类算法，并建立了不同结节间纹理集的距离评估方法。我们的算法在囊性和实性区域的像素级分类准确率为0.931，低回声区域为0.870，等回声区域为0.959，高回声区域为0.961。我们算法的有效性及其与人类观察结果的一致性已得到证明。此外，我们进行了计算并可视化了良性和恶性结节中不同纹理的分布。该方法可用于自动生成甲状腺结节纹理的像素级标签，有助于纹理数据集的构建，并为医学专业人员提供图像分析信息。
