Research progress on deep learning-based computer-aided diagnosis of thyroid nodules using ultrasound imaging_Journal of Biomedical Engineering

Authors：

ZHOU Xinyuan ¹ , Qiu Min ² ,  SHANG Jiangfeng ³ ,  WEI Guohui ¹

1. School of Medical Information Engineering, Shandong University of Traditional Chinese Medicine, Jinan 250355, P. R. China;
2. Department of Thyroid Surgery, Affiliated Hosipital of Jining Medical University, Jining, Shandong 272007, P. R. China;
3. Department of Thyroid and Breast Surgery, Changshu Hospital Affiliated to Suzhou University (Changshu NO.1 People's Hospital), Suzhou, Jiangsu 215500, P. R. China;

Corresponding author：

Keywords：

Thyroid nodules; Deep learning; Ultrasonic image; Computer aided diagnosis

DOI：

10.7507/1001-5515.202412047

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

Thyroid nodules are a common endocrine disorder, and their early detection and accurate diagnosis are crucial for the prevention of thyroid cancer. However, the highly heterogeneous morphology and boundaries of thyroid nodules pose significant challenges to their precise identification and classification. Traditional diagnostic approaches rely heavily on physicians’ experience, which increases the risk of misdiagnosis and missed diagnoses. With the rapid advancement of computer-aided diagnosis (CAD) technologies, applying deep learning algorithms to the analysis of thyroid nodule ultrasound images has shown great potential. This paper reviews the latest research progress on deep learning-based CAD methods for thyroid nodules, with a focus on their applications in image preprocessing, segmentation and classification. The advantages and limitations of current techniques are analyzed, and potential future directions are discussed. This review aims to highlight the potential of deep learning in thyroid nodule diagnosis and to provide a foundation for selecting feasible pathways for future clinical applications.

1.	中华医学会内分泌学分会, 中华医学会外科学分会甲状腺及代谢外科学组, 中国抗癌协会头颈肿瘤专业委员会, 等. 甲状腺结节和分化型甲状腺癌诊治指南(第二版). 国际内分泌代谢杂志, 2023, 43(2): 149-194.
2.	何美情, 张均, 高燕华, 等. 深度学习联合C-TIRADS在甲状腺4a类结节风险分层管理的应用. 分子影像学杂志, 2024, 47(9): 921-927.
3.	刘娜, 刘婷, 马盼, 等. 基于深度学习方法的诊断模型在甲状腺结节超声诊断教学中的应用研究. 西部素质教育, 2024, 10(22): 131-134.
4.	Li M, Dal Maso L, Vaccarella S. Global trends in thyroid cancer incidence and the impact of overdiagnosis. Lancet Diabetes Endocrinol, 2020, 8(6): 468-470.
5.	de Carlos J, Garcia J, Basterra F J, et al. Interobserver variability in thyroid ultrasound. Endocrine, 2024, 85(2): 730-736.
6.	Kim P H, Suh C H, Baek J H, et al. Unnecessary thyroid nodule biopsy rates under four ultrasound risk stratification systems: a systematic review and meta-analysis. European Radiology, 2021, 31: 2877-2885.
7.	Jin Z, Zhu Y, Zhang S, et al. Ultrasound computer-aided diagnosis (CAD) based on the thyroid imaging reporting and data system (TI-RADS) to distinguish benign from malignant thyroid nodules and the diagnostic performance of radiologists with different diagnostic experience. Med Sci Monit, 2020, 26: e918452.
8.	梁光威. 基于深度学习的甲状腺结节超声图像辅助诊断研究. 哈尔滨: 哈尔滨理工大学, 2024.
9.	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.
10.	Chen Y, Guo Z, Yuan J, et al. Dual-TranSpeckle: dual-pathway transformer based encoder-decoder network for medical ultrasound image despeckling. Computers in Biology and Medicine, 2024, 173: 108313.
11.	Lin X, Zhou X, Tong T, et al. A super-resolution guided network for improving automated thyroid nodule segmentation. Computer Methods and Programs in Biomedicine, 2022, 227: 107186.
12.	Kang C, Jiao L, Wang R, et al. Attention-based multiscale feature pyramid network for corn pest detection under wild environment. Insects, 2022, 13(11): 978.
13.	Liu Z, Lin Y, Cao Y, et al. Swin transformer: hierarchical vision transformer using shifted windows//Proceedings of the IEEE/ CVF International Conference on Computer Vision. 2021: 10012-10022.
14.	Wang J, Yang X, Jia X, et al. Thyroid ultrasound diagnosis improvement via multi-view self-supervised learning and two-stage pre-training. Computers in Biology and Medicine, 2024, 171: 108087.
15.	Yang W T, Ma B Y, Chen Y. A narrative review of deep learning in thyroid imaging: current progress and future prospects. Quant Imaging Med Surg, 2024, 14(2): 2069-2088.
16.	Minaee S, Boykov Y, Porikli F, et al. Image segmentation using deep learning: a survey. IEEE Trans Pattern Anal Mach Intell, 2022, 44(7): 3523-3542.
17.	Yu Y, Wang C, Fu Q, et al. Techniques and challenges of image segmentation: a review. Electronics, 2023, 12(5): 1199.
18.	Xu Y, Quan R, Xu W, et al. Advances in medical image segmentation: a comprehensive review of traditional, deep learning and hybrid approaches. Bioengineering, 2024, 11(10): 1034.
19.	Dong P, Zhang R, Li J, et al. An ultrasound image segmentation method for thyroid nodules based on dual-path attention mechanism-enhanced UNet++. BMC Medical Imaging, 2024, 24(1): 341.
20.	Tao Z, Dang H, Shi Y, et al. Local and context-attention adaptive LCA-Net for thyroid nodule segmentation in ultrasound images. Sensors, 2022, 22(16): 5984.
21.	Kang Q, Lao Q, Li Y, et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical Image Analysis, 2022, 79: 102443.
22.	Gong H, Chen J, Chen G, et al. Thyroid region prior guided attention for ultrasound segmentation of thyroid nodules. Comput Biol Med, 2023, 155: 106389.
23.	Bi H, Cai C, Sun J, et al. BPAT-UNet: boundary preserving assembled transformer UNet for ultrasound thyroid nodule segmentation. Comput Methods Programs Biomed, 2023, 238: 107614.
24.	Ma X, Sun B, Liu W, et al. Tnseg: adversarial networks with multi-scale joint loss for thyroid nodule segmentation. The Journal of Supercomputing, 2024, 80(5): 6093-6118.
25.	Sun X, Wei B, Jiang Y, et al. CLIP-TNseg: a multi-modal hybrid framework for thyroid nodule segmentation in ultrasound images. IEEE Signal Processing Letters, 2025, 32(1): 2025-2029.
26.	Chen H, Cai Y, Wang C, et al. Multi-organ foundation model for universal ultrasound image segmentation with task prompt and anatomical prior. IEEE Transactions on Medical Imaging, 2025, 44(2): 1005-1018.
27.	Srivastava R, Kumar P. Deep-GAN: an improved model for thyroid nodule identification and classification. Neural Computing and Applications, 2024, 36(14): 7685-7704.
28.	Yang T Y, Zhou L Q, Han X H, et al. An improved CNN-based thyroid nodule screening algorithm in ultrasound images. Biomedical Signal Processing and Control, 2024, 87: 105371.
29.	Tareke T W, Leclerc S, Vuillemin C, et al. Automatic classification of nodules from 2D ultrasound images using deep learning networks. Journal of Imaging, 2024, 10(8): 203.
30.	Rahman M A, Joy A, Abir A T, et al. Unleashing the power of open-source transformers in medical imaging: insights from a brain. International Journal of Advanced Computer Science & Applications, 2024, 15(7): 126-131.
31.	Swathi G, Altalbe A, Kumar R P. QuCNet: quantum-inspired convolutional neural networks for optimized thyroid nodule classification. IEEE Access, 2024, 12: 27829-27842.
32.	Sun J, Wu B, Zhao T, et al. Classification for thyroid nodule using ViT with contrastive learning in ultrasound images. Comput Biol Med, 2023, 152: 106444.
33.	Jerbi F, Aboudi N, Khlifa N. Automatic classification of ultrasound thyroids images using vision transformers and generative adversarial networks. Scientific African, 2023, 20: e01679.
34.	Baima N, Wang T, Zhao C K, et al. Dense Swin Transformer for classification of thyroid nodules. Annu Int Conf IEEE Eng Med Biol Soc, 2023, 2023: 1-4.
35.	Huang L, Xu Y, Wang S, et al. SRT: Swin-residual transformer for benign and malignant nodules classification in thyroid ultrasound images. Medical Engineering & Physics, 2024, 124: 104101.
36.	Gu A, Dao T. Mamba: linear-time sequence modeling with selective state spaces. arXiv preprint, 2023, arXiv: 2312.00752.
37.	Ruan J, Li J, Xiang S. VM-Unet: vision Mamba Unet for medical image segmentation. arXiv preprint, 2024, arXiv: 2402.02491, .
38.	Nasiri-Sarvi A, Trinh V Q H, Rivaz H, et al. Vim4path: self-supervised vision Mamba for histopathology images//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024: 6894-6903.
39.	Nguyen T D, Nguyen T, Le Nguyen P, et al. Backdoor attacks and defenses in federated learning: Survey, challenges and future research directions. Engineering Applications of Artificial Intelligence, 2024, 127: 107166.
40.	Fan F L, Xiong J, Li M, et al. On interpretability of artificial neural networks: a survey. IEEE Transactions on Radiation and Plasma Medical Sciences, 2021, 5(6): 741-760.
41.	Tong W J, Wu S H, Cheng M Q, et al. Integration of artificial intelligence decision aids to reduce workload and enhance efficiency in thyroid nodule management. JAMA Network Open, 2023, 6(5): e2313674.

1. 中华医学会内分泌学分会, 中华医学会外科学分会甲状腺及代谢外科学组, 中国抗癌协会头颈肿瘤专业委员会, 等. 甲状腺结节和分化型甲状腺癌诊治指南(第二版). 国际内分泌代谢杂志, 2023, 43(2): 149-194.
2. 何美情, 张均, 高燕华, 等. 深度学习联合C-TIRADS在甲状腺4a类结节风险分层管理的应用. 分子影像学杂志, 2024, 47(9): 921-927.
3. 刘娜, 刘婷, 马盼, 等. 基于深度学习方法的诊断模型在甲状腺结节超声诊断教学中的应用研究. 西部素质教育, 2024, 10(22): 131-134.
4. Li M, Dal Maso L, Vaccarella S. Global trends in thyroid cancer incidence and the impact of overdiagnosis. Lancet Diabetes Endocrinol, 2020, 8(6): 468-470.
5. de Carlos J, Garcia J, Basterra F J, et al. Interobserver variability in thyroid ultrasound. Endocrine, 2024, 85(2): 730-736.
6. Kim P H, Suh C H, Baek J H, et al. Unnecessary thyroid nodule biopsy rates under four ultrasound risk stratification systems: a systematic review and meta-analysis. European Radiology, 2021, 31: 2877-2885.
7. Jin Z, Zhu Y, Zhang S, et al. Ultrasound computer-aided diagnosis (CAD) based on the thyroid imaging reporting and data system (TI-RADS) to distinguish benign from malignant thyroid nodules and the diagnostic performance of radiologists with different diagnostic experience. Med Sci Monit, 2020, 26: e918452.
8. 梁光威. 基于深度学习的甲状腺结节超声图像辅助诊断研究. 哈尔滨: 哈尔滨理工大学, 2024.
9. 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.
10. Chen Y, Guo Z, Yuan J, et al. Dual-TranSpeckle: dual-pathway transformer based encoder-decoder network for medical ultrasound image despeckling. Computers in Biology and Medicine, 2024, 173: 108313.
11. Lin X, Zhou X, Tong T, et al. A super-resolution guided network for improving automated thyroid nodule segmentation. Computer Methods and Programs in Biomedicine, 2022, 227: 107186.
12. Kang C, Jiao L, Wang R, et al. Attention-based multiscale feature pyramid network for corn pest detection under wild environment. Insects, 2022, 13(11): 978.
13. Liu Z, Lin Y, Cao Y, et al. Swin transformer: hierarchical vision transformer using shifted windows//Proceedings of the IEEE/ CVF International Conference on Computer Vision. 2021: 10012-10022.
14. Wang J, Yang X, Jia X, et al. Thyroid ultrasound diagnosis improvement via multi-view self-supervised learning and two-stage pre-training. Computers in Biology and Medicine, 2024, 171: 108087.
15. Yang W T, Ma B Y, Chen Y. A narrative review of deep learning in thyroid imaging: current progress and future prospects. Quant Imaging Med Surg, 2024, 14(2): 2069-2088.
16. Minaee S, Boykov Y, Porikli F, et al. Image segmentation using deep learning: a survey. IEEE Trans Pattern Anal Mach Intell, 2022, 44(7): 3523-3542.
17. Yu Y, Wang C, Fu Q, et al. Techniques and challenges of image segmentation: a review. Electronics, 2023, 12(5): 1199.
18. Xu Y, Quan R, Xu W, et al. Advances in medical image segmentation: a comprehensive review of traditional, deep learning and hybrid approaches. Bioengineering, 2024, 11(10): 1034.
19. Dong P, Zhang R, Li J, et al. An ultrasound image segmentation method for thyroid nodules based on dual-path attention mechanism-enhanced UNet++. BMC Medical Imaging, 2024, 24(1): 341.
20. Tao Z, Dang H, Shi Y, et al. Local and context-attention adaptive LCA-Net for thyroid nodule segmentation in ultrasound images. Sensors, 2022, 22(16): 5984.
21. Kang Q, Lao Q, Li Y, et al. Thyroid nodule segmentation and classification in ultrasound images through intra-and inter-task consistent learning. Medical Image Analysis, 2022, 79: 102443.
22. Gong H, Chen J, Chen G, et al. Thyroid region prior guided attention for ultrasound segmentation of thyroid nodules. Comput Biol Med, 2023, 155: 106389.
23. Bi H, Cai C, Sun J, et al. BPAT-UNet: boundary preserving assembled transformer UNet for ultrasound thyroid nodule segmentation. Comput Methods Programs Biomed, 2023, 238: 107614.
24. Ma X, Sun B, Liu W, et al. Tnseg: adversarial networks with multi-scale joint loss for thyroid nodule segmentation. The Journal of Supercomputing, 2024, 80(5): 6093-6118.
25. Sun X, Wei B, Jiang Y, et al. CLIP-TNseg: a multi-modal hybrid framework for thyroid nodule segmentation in ultrasound images. IEEE Signal Processing Letters, 2025, 32(1): 2025-2029.
26. Chen H, Cai Y, Wang C, et al. Multi-organ foundation model for universal ultrasound image segmentation with task prompt and anatomical prior. IEEE Transactions on Medical Imaging, 2025, 44(2): 1005-1018.
27. Srivastava R, Kumar P. Deep-GAN: an improved model for thyroid nodule identification and classification. Neural Computing and Applications, 2024, 36(14): 7685-7704.
28. Yang T Y, Zhou L Q, Han X H, et al. An improved CNN-based thyroid nodule screening algorithm in ultrasound images. Biomedical Signal Processing and Control, 2024, 87: 105371.
29. Tareke T W, Leclerc S, Vuillemin C, et al. Automatic classification of nodules from 2D ultrasound images using deep learning networks. Journal of Imaging, 2024, 10(8): 203.
30. Rahman M A, Joy A, Abir A T, et al. Unleashing the power of open-source transformers in medical imaging: insights from a brain. International Journal of Advanced Computer Science & Applications, 2024, 15(7): 126-131.
31. Swathi G, Altalbe A, Kumar R P. QuCNet: quantum-inspired convolutional neural networks for optimized thyroid nodule classification. IEEE Access, 2024, 12: 27829-27842.
32. Sun J, Wu B, Zhao T, et al. Classification for thyroid nodule using ViT with contrastive learning in ultrasound images. Comput Biol Med, 2023, 152: 106444.
33. Jerbi F, Aboudi N, Khlifa N. Automatic classification of ultrasound thyroids images using vision transformers and generative adversarial networks. Scientific African, 2023, 20: e01679.
34. Baima N, Wang T, Zhao C K, et al. Dense Swin Transformer for classification of thyroid nodules. Annu Int Conf IEEE Eng Med Biol Soc, 2023, 2023: 1-4.
35. Huang L, Xu Y, Wang S, et al. SRT: Swin-residual transformer for benign and malignant nodules classification in thyroid ultrasound images. Medical Engineering & Physics, 2024, 124: 104101.
36. Gu A, Dao T. Mamba: linear-time sequence modeling with selective state spaces. arXiv preprint, 2023, arXiv: 2312.00752.
37. Ruan J, Li J, Xiang S. VM-Unet: vision Mamba Unet for medical image segmentation. arXiv preprint, 2024, arXiv: 2402.02491, .
38. Nasiri-Sarvi A, Trinh V Q H, Rivaz H, et al. Vim4path: self-supervised vision Mamba for histopathology images//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024: 6894-6903.
39. Nguyen T D, Nguyen T, Le Nguyen P, et al. Backdoor attacks and defenses in federated learning: Survey, challenges and future research directions. Engineering Applications of Artificial Intelligence, 2024, 127: 107166.
40. Fan F L, Xiong J, Li M, et al. On interpretability of artificial neural networks: a survey. IEEE Transactions on Radiation and Plasma Medical Sciences, 2021, 5(6): 741-760.
41. Tong W J, Wu S H, Cheng M Q, et al. Integration of artificial intelligence decision aids to reduce workload and enhance efficiency in thyroid nodule management. JAMA Network Open, 2023, 6(5): e2313674.

Journal of Biomedical Engineering

Latest ArticlesResearch progress on deep learning-based computer-aided diagnosis of thyroid nodules using ultrasound imaging

Abstract Full text Figures/Tables Video References Cited by

Format

Content