Review on ultrasonographic diagnosis of thyroid diseases based on deep learning_Journal of Biomedical Engineering

Authors：

QI Fengyuan ¹ , QIU Min ² ,  WEI Guohui ¹

1. College of Intelligence and Information Engineering, Shandong University of Traditional Chinese Medicine, Jinan 250355, P. R. China;
2. Department of Thyroid Surgery, Affiliated Hospital of Jining Medical University, Jining, Shandong 272007, P. R. China;

Corresponding author：

Keywords：

Deep learning; Thyroid disease; Ultrasonic image; Multimodal image

DOI：

10.7507/1001-5515.202302049

Video：

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In recent years, the incidence of thyroid diseases has increased significantly and ultrasound examination is the first choice for the diagnosis of thyroid diseases. At the same time, the level of medical image analysis based on deep learning has been rapidly improved. Ultrasonic image analysis has made a series of milestone breakthroughs, and deep learning algorithms have shown strong performance in the field of medical image segmentation and classification. This article first elaborates on the application of deep learning algorithms in thyroid ultrasound image segmentation, feature extraction, and classification differentiation. Secondly, it summarizes the algorithms for deep learning processing multimodal ultrasound images. Finally, it points out the problems in thyroid ultrasound image diagnosis at the current stage and looks forward to future development directions. This study can promote the application of deep learning in clinical ultrasound image diagnosis of thyroid, and provide reference for doctors to diagnose thyroid disease.

Citation： QI Fengyuan, QIU Min, WEI Guohui. Review on ultrasonographic diagnosis of thyroid diseases based on deep learning. Journal of Biomedical Engineering, 2023, 40(5): 1027-1032. doi: 10.7507/1001-5515.202302049 Copy

1.	Sung H, Ferlay J, Siegel R L, et al. Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a Cancer Journal for Clinicians, 2021, 71(3): 209-249.
2.	Li M, Dal Maso L, Vaccarella S. Global trends in thyroid cancer incidence and the impact of overdiagnosis. The Lancet Diabetes & Endocrinology, 2020, 8(6): 468-470.
3.	Ngu R. Ultrasonography in dentomaxillofacial diagnostics. Switzerland: Springer Nature Switzerland AG, 2021: 319-336.
4.	Tessler F N, Middleton W D, Grant E G, et al. ACR thyroid imaging, reporting and data system (TI-RADS): white paper of the ACR TI-RADS committee. Journal of the American College of Radiology, 2017, 14(5): 587-595.
5.	Chan H P, Samala R K, Hadjiiski L M, et al. Deep learning in medical image analysis: challenges and applications. Switzerland: Springer Nature Switzerland AG, 2020: 3-21.
6.	Chen J, You H, Li K. A review of thyroid gland segmentation and thyroid nodule segmentation methods for medical ultrasound images. Computer Methods and Programs in Biomedicine, 2020, 185: 105329.
7.	Cao Y, Zhong X, Diao W, et al. Radiomics in differentiated thyroid cancer and nodules: explorations, application, and limitations. Cancers, 2021, 13(10): 2436.
8.	Sharifi Y, Bakhshali M A, Dehghani T, et al. Deep learning on ultrasound images of thyroid nodules. Biocybernetics and Biomedical Engineering, 2021, 41(2): 636-655.
9.	Sagheer S V M, George S N. A review on medical image denoising algorithms. Biomedical Signal Processing and Control, 2020, 61: 102036.
10.	Kumar V, Webb J, Gregory A, et al. Automated segmentation of thyroid nodule, gland, and cystic components from ultrasound images using deep learning. IEEE Access, 2020, 8: 63482-63496.
11.	Chu C, Zheng J, Zhou Y. Ultrasonic thyroid nodule detection method based on U-Net network. Computer Methods and Programs in Biomedicine, 2021, 199: 105906.
12.	Wu J, Zhang Z, Zhao J, et al. Ultrasound image segmentation of thyroid nodules based on joint up-sampling//The 2020 Second International Conference on Artificial Intelligence Technologies and Application (ICAITA), Dalian: AEIC-Academic Exchange Information Center, 2020, 1651(1): 012157.
13.	Ding J, Huang Z, Shi M, et al. Automatic thyroid ultrasound image segmentation based on U-shaped network//2019 12th International Congress on Image and Signal Processing, Biomedical Engineering and Informatics (CISP-BMEI), Suzhou: IEEE, 2019: 1-5.
14.	Yang Q, Geng C, Chen R, et al. DMU-Net: dual-route mirroring U-Net with mutual learning for malignant thyroid nodule segmentation. Biomedical Signal Processing and Control, 2022, 77: 103805.
15.	Webb J M, Meixner D D, Adusei S A, et al. Automatic deep learning semantic segmentation of ultrasound thyroid cineclips using recurrent fully convolutional networks. IEEE Access, 2020, 9: 5119-5127.
16.	Sun J, Li C, Lu Z, et al. TNSNet: Thyroid nodule segmentation in ultrasound imaging using soft shape supervision. Computer Methods and Programs in Biomedicine, 2022, 215: 106600.
17.	Buda M, Wildman-Tobriner B, Castor K, et al. Deep learning-based segmentation of nodules in thyroid ultrasound: improving performance by utilizing markers present in the images. Ultrasound in Medicine & Biology, 2020, 46(2): 415-421.
18.	Zhu Y C, AlZoubi A, Jassim S, et al. A generic deep learning framework to classify thyroid and breast lesions in ultrasound images. Ultrasonics, 2021, 110: 106300.
19.	Zhao X, Shen X, Wan W, et al. Automatic thyroid ultrasound image classification using feature fusion network. IEEE Access, 2022, 10: 27917-27924.
20.	Song R, Zhang L, Zhu C, et al. Thyroid nodule ultrasound image classification through hybrid feature cropping network. IEEE Access, 2020, 8: 64064-64074.
21.	Wang L, Zhang L, Zhu M, et al. Automatic diagnosis for thyroid nodules in ultrasound images by deep neural networks. Medical image analysis, 2020, 61: 101665.
22.	Tasnimi M, Ghaffari H R. Diagnosis of anomalies based on hybrid features extraction in thyroid images. Multimedia Tools and Applications, 2023, 82(3): 3859-3877.
23.	Baloch Z W, Asa S L, Barletta J A, et al. Overview of the 2022 WHO classification of thyroid neoplasms. Endocrine Pathology, 2022, 33(1): 27-63.
24.	Guan Q, Wang Y, Du J, et al. Deep learning based classification of ultrasound images for thyroid nodules: a large scale of pilot study. Annals of Translational Medicine, 2019, 7(7): 137.
25.	张烽, 翁英健, 苏家明, 等. 基于TV模型与GoogLeNet的甲状腺结节图像分类. 计算机应用研究, 2020, 37(S1): 421-422, 417.
26.	Wei X, Gao M, Yu R, et al. Ensemble deep learning model for multicenter classification of thyroid nodules on ultrasound images. Medical Science Monito, 2020, 26: e926096.
27.	Vasile C M, Udriștoiu A L, Ghenea A E, et al. Intelligent diagnosis of thyroid ultrasound imaging using an ensemble of deep learning methods. Medicina (Kaunas), 2021, 57(4): 395.
28.	Chan W K, Sun J H, Liou M J, et al. Using deep convolutional neural networks for enhanced ultrasonographic image diagnosis of differentiated thyroid cancer. Biomedicines, 2021, 9(12): 1771.
29.	Han Z, Huang Y, Wang H, et al. Multimodal ultrasound imaging: a method to improve the accuracy of diagnosing thyroid TI‐RADS 4 nodules. Journal of Clinical Ultrasound, 2022, 50(9): 1345-1352.
30.	Zhao J, Zhou X, Shi G, et al. Semantic consistency generative adversarial network for cross-modality domain adaptation in ultrasound thyroid nodule classification. Applied Intelligence (Dordr), 2022, 52: 10369-10383.
31.	Yang W, Dong Y, Du Q, et al. Integrate domain knowledge in training multi-task cascade deep learning model for benign–malignant thyroid nodule classification on ultrasound images. Engineering Applications of Artificial Intelligence, 2021, 98: 104064.
32.	武宽, 秦品乐, 柴锐, 等. 基于不同超声成像的甲状腺结节良恶性判别. 计算机应用, 2020, 40(1): 77-82.
33.	Xiang Z, Zhuo Q, Zhao C, et al. Self-supervised multi-modal fusion network for multi-modal thyroid ultrasound image diagnosis. Computers in Biology and Medicine, 2022, 150: 106164.
34.	Yu X, Wang H, Ma L. Detection of thyroid nodules with ultrasound images based on deep learning. Current Medical Imaging, 2020, 16(2): 174-180.
35.	Chai Y J, Song J, Shaear M, et al. Artificial intelligence for thyroid nodule ultrasound image analysis. Annals of Thyroid, 2020, 5: 8.
36.	Shi G, Wang J, Qiang Y, et al. Knowledge-guided synthetic medical image adversarial augmentation for ultrasonography thyroid nodule classification. Computer Methods and Programs in Biomedicine, 2020, 196: 105611.
37.	Seo J K, Kim Y J, Kim K G, et al. Differentiation of the follicular neoplasm on the gray-scale US by image selection subsampling along with the marginal outline using convolutional neural network. BioMed Research International, 2017, 2017: 3098293.
38.	Zhou H, Jin Y, Dai L, et al. Differential diagnosis of benign and malignant thyroid nodules using deep learning radiomics of thyroid ultrasound images. European Journal of Radiology, 2020, 127: 108992.
39.	Wu X, Li M, Cui X, et al. Deep multimodal learning for lymph node metastasis prediction of primary thyroid cancer. Physics in Medicine & Biology, 2022, 67(3): 035008.

1. Sung H, Ferlay J, Siegel R L, et al. Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a Cancer Journal for Clinicians, 2021, 71(3): 209-249.
2. Li M, Dal Maso L, Vaccarella S. Global trends in thyroid cancer incidence and the impact of overdiagnosis. The Lancet Diabetes & Endocrinology, 2020, 8(6): 468-470.
3. Ngu R. Ultrasonography in dentomaxillofacial diagnostics. Switzerland: Springer Nature Switzerland AG, 2021: 319-336.
4. Tessler F N, Middleton W D, Grant E G, et al. ACR thyroid imaging, reporting and data system (TI-RADS): white paper of the ACR TI-RADS committee. Journal of the American College of Radiology, 2017, 14(5): 587-595.
5. Chan H P, Samala R K, Hadjiiski L M, et al. Deep learning in medical image analysis: challenges and applications. Switzerland: Springer Nature Switzerland AG, 2020: 3-21.
6. Chen J, You H, Li K. A review of thyroid gland segmentation and thyroid nodule segmentation methods for medical ultrasound images. Computer Methods and Programs in Biomedicine, 2020, 185: 105329.
7. Cao Y, Zhong X, Diao W, et al. Radiomics in differentiated thyroid cancer and nodules: explorations, application, and limitations. Cancers, 2021, 13(10): 2436.
8. Sharifi Y, Bakhshali M A, Dehghani T, et al. Deep learning on ultrasound images of thyroid nodules. Biocybernetics and Biomedical Engineering, 2021, 41(2): 636-655.
9. Sagheer S V M, George S N. A review on medical image denoising algorithms. Biomedical Signal Processing and Control, 2020, 61: 102036.
10. Kumar V, Webb J, Gregory A, et al. Automated segmentation of thyroid nodule, gland, and cystic components from ultrasound images using deep learning. IEEE Access, 2020, 8: 63482-63496.
11. Chu C, Zheng J, Zhou Y. Ultrasonic thyroid nodule detection method based on U-Net network. Computer Methods and Programs in Biomedicine, 2021, 199: 105906.
12. Wu J, Zhang Z, Zhao J, et al. Ultrasound image segmentation of thyroid nodules based on joint up-sampling//The 2020 Second International Conference on Artificial Intelligence Technologies and Application (ICAITA), Dalian: AEIC-Academic Exchange Information Center, 2020, 1651(1): 012157.
13. Ding J, Huang Z, Shi M, et al. Automatic thyroid ultrasound image segmentation based on U-shaped network//2019 12th International Congress on Image and Signal Processing, Biomedical Engineering and Informatics (CISP-BMEI), Suzhou: IEEE, 2019: 1-5.
14. Yang Q, Geng C, Chen R, et al. DMU-Net: dual-route mirroring U-Net with mutual learning for malignant thyroid nodule segmentation. Biomedical Signal Processing and Control, 2022, 77: 103805.
15. Webb J M, Meixner D D, Adusei S A, et al. Automatic deep learning semantic segmentation of ultrasound thyroid cineclips using recurrent fully convolutional networks. IEEE Access, 2020, 9: 5119-5127.
16. Sun J, Li C, Lu Z, et al. TNSNet: Thyroid nodule segmentation in ultrasound imaging using soft shape supervision. Computer Methods and Programs in Biomedicine, 2022, 215: 106600.
17. Buda M, Wildman-Tobriner B, Castor K, et al. Deep learning-based segmentation of nodules in thyroid ultrasound: improving performance by utilizing markers present in the images. Ultrasound in Medicine & Biology, 2020, 46(2): 415-421.
18. Zhu Y C, AlZoubi A, Jassim S, et al. A generic deep learning framework to classify thyroid and breast lesions in ultrasound images. Ultrasonics, 2021, 110: 106300.
19. Zhao X, Shen X, Wan W, et al. Automatic thyroid ultrasound image classification using feature fusion network. IEEE Access, 2022, 10: 27917-27924.
20. Song R, Zhang L, Zhu C, et al. Thyroid nodule ultrasound image classification through hybrid feature cropping network. IEEE Access, 2020, 8: 64064-64074.
21. Wang L, Zhang L, Zhu M, et al. Automatic diagnosis for thyroid nodules in ultrasound images by deep neural networks. Medical image analysis, 2020, 61: 101665.
22. Tasnimi M, Ghaffari H R. Diagnosis of anomalies based on hybrid features extraction in thyroid images. Multimedia Tools and Applications, 2023, 82(3): 3859-3877.
23. Baloch Z W, Asa S L, Barletta J A, et al. Overview of the 2022 WHO classification of thyroid neoplasms. Endocrine Pathology, 2022, 33(1): 27-63.
24. Guan Q, Wang Y, Du J, et al. Deep learning based classification of ultrasound images for thyroid nodules: a large scale of pilot study. Annals of Translational Medicine, 2019, 7(7): 137.
25. 张烽, 翁英健, 苏家明, 等. 基于TV模型与GoogLeNet的甲状腺结节图像分类. 计算机应用研究, 2020, 37(S1): 421-422, 417.
26. Wei X, Gao M, Yu R, et al. Ensemble deep learning model for multicenter classification of thyroid nodules on ultrasound images. Medical Science Monito, 2020, 26: e926096.
27. Vasile C M, Udriștoiu A L, Ghenea A E, et al. Intelligent diagnosis of thyroid ultrasound imaging using an ensemble of deep learning methods. Medicina (Kaunas), 2021, 57(4): 395.
28. Chan W K, Sun J H, Liou M J, et al. Using deep convolutional neural networks for enhanced ultrasonographic image diagnosis of differentiated thyroid cancer. Biomedicines, 2021, 9(12): 1771.
29. Han Z, Huang Y, Wang H, et al. Multimodal ultrasound imaging: a method to improve the accuracy of diagnosing thyroid TI‐RADS 4 nodules. Journal of Clinical Ultrasound, 2022, 50(9): 1345-1352.
30. Zhao J, Zhou X, Shi G, et al. Semantic consistency generative adversarial network for cross-modality domain adaptation in ultrasound thyroid nodule classification. Applied Intelligence (Dordr), 2022, 52: 10369-10383.
31. Yang W, Dong Y, Du Q, et al. Integrate domain knowledge in training multi-task cascade deep learning model for benign–malignant thyroid nodule classification on ultrasound images. Engineering Applications of Artificial Intelligence, 2021, 98: 104064.
32. 武宽, 秦品乐, 柴锐, 等. 基于不同超声成像的甲状腺结节良恶性判别. 计算机应用, 2020, 40(1): 77-82.
33. Xiang Z, Zhuo Q, Zhao C, et al. Self-supervised multi-modal fusion network for multi-modal thyroid ultrasound image diagnosis. Computers in Biology and Medicine, 2022, 150: 106164.
34. Yu X, Wang H, Ma L. Detection of thyroid nodules with ultrasound images based on deep learning. Current Medical Imaging, 2020, 16(2): 174-180.
35. Chai Y J, Song J, Shaear M, et al. Artificial intelligence for thyroid nodule ultrasound image analysis. Annals of Thyroid, 2020, 5: 8.
36. Shi G, Wang J, Qiang Y, et al. Knowledge-guided synthetic medical image adversarial augmentation for ultrasonography thyroid nodule classification. Computer Methods and Programs in Biomedicine, 2020, 196: 105611.
37. Seo J K, Kim Y J, Kim K G, et al. Differentiation of the follicular neoplasm on the gray-scale US by image selection subsampling along with the marginal outline using convolutional neural network. BioMed Research International, 2017, 2017: 3098293.
38. Zhou H, Jin Y, Dai L, et al. Differential diagnosis of benign and malignant thyroid nodules using deep learning radiomics of thyroid ultrasound images. European Journal of Radiology, 2020, 127: 108992.
39. Wu X, Li M, Cui X, et al. Deep multimodal learning for lymph node metastasis prediction of primary thyroid cancer. Physics in Medicine & Biology, 2022, 67(3): 035008.

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