Automatic Segmentation and Classification of Thyroid Nodules in Ultrasound Images with Convolutional Neural Networks

• Published in: Segmentation, Classification, and Registration of Multi-Modality Medical Imaging Data Vol. 12587; pp. 109 - 115
• Main Authors: Wang, Mingyu, Yuan, Chenglang, Wu, Dasheng, Zeng, Yinghou, Zhong, Shaonan, Qiu, Weibao
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2021; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Convolutional neural network; Thyroid nodule; Ultrasound image
• ISBN: 9783030718268; 3030718263
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-71827-5_14

Ultrasound image plays an important role in the diagnosis of thyroid disease. Accurate segmentation and classification of thyroid nodules are challenging due to their heterogeneous appearance. In this paper, we propose an efficient cascaded segmentation framework and a dual-attention ResNet-based classification network to automatically achieve the accurate segmentation and classification of thyroid nodules, respectively. We evaluate our methods on the training dataset TN-SCUI 2020 Challenge. The 5-fold cross validation results demonstrate that the proposed methods achieve average IoU of 81.43% in segmentation task, and average F1 score of 83.22% in classification task. Finally, our method ranks the first place of segmentation task on the test set through the final online verification. The source code of the proposed methods is available at https://github.com/WAMAWAMA/TNSCUI2020-Seg-Rank1st.