Ultrasound image-based thyroid nodule automatic segmentation using convolutional neural networks
Abstract
Purpose Delineation of thyroid nodule boundaries from ultrasound images plays an important role in calculation of clinical indices and diagnosis of thyroid diseases. However, it is challenging for accurate and automatic segmentation of thyroid nodules because of their heterogeneous appearance and components similar to the background. In this study, we employ a deep convolutional neural network (CNN) to automatically segment thyroid nodules from ultrasound images.Methods Our CNN-based method formulates a thyroid nodule segmentation problem as a patch classification task, where the relationship among patches is ignored. Specifically, the CNN used image patches from images of normal thyroids and thyroid nodules as inputs and then generated the segmentation probability maps as outputs. A multi-view strategy is used to improve the performance of the CNN-based model. Additionally, we compared the performance of our approach with that of the commonly used segmentation methods on the same dataset.Results The experimental results suggest that our proposed method outperforms prior methods on thyroid nodule segmentation. Moreover, the results show that the CNN-based model is able to delineate multiple nodules in thyroid ultrasound images accurately and effectively. In detail, our CNN-based model can achieve an average of the overlap metric, dice ratio, true positive rate, false positive rate, and modified Hausdorff distance as [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text] on overall folds, respectively.Conclusion Our proposed method is fully automatic without any user interaction. Quantitative results also indicate that our method is so efficient and accurate that it can be good enough to replace the time-consuming and tedious manual segmentation approach, demonstrating the potential clinical applications.
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Formulating segmentation as a patch-level classification task can significantly reduce extensive computation cost and memory requirement.
“This method can significantly reduce the extensive computation cost and memory requirement.”
From Deep Learning in Medical Ultrasound Analysis: A Review (2019) · cites this paper as [125]
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Deep Learning in Medical Ultrasound Analysis: A Review
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- Authors
- Ma, J., Wu, F., Jiang, T., Zhao, Q., & Kong, D.
- Affiliations
- Zhejiang University (CN)
- First Affiliated Hospital Zhejiang University (CN)
- Venue
- International Journal of Computer Assisted Radiology and Surgery
- Published
- 2017-07-31
- Volume
- 12
- Issue
- 11
- Pages
- 1895-1910
- ISSN
- 1861-6410 (ISSN-L)
- 1861-6429
- OpenAlex
- W2741754476
- Cited by
- 187
- References
- 27
- Citations by year
- 2026: 8 · 2025: 25 · 2024: 24 · 2023: 29 · 2022: 35 · 2021: 29 · 2020: 6 · 2019: 20 · 2018: 11
- FWCI
- 4.20
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Computer Assisted Radiology and Surgery (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Thyroid Cancer Diagnosis and Treatment
- Keywords
- thyroid nodule segmentation, thyroid ultrasound, convolutional neural networks, patch-based classification, automatic segmentation, thyroid dysfunction
- MeSH terms
- Adult, Aged, Female, Humans, Image Processing, Computer-Assisted, Male, Middle Aged, Probability, Ultrasonography, Neural Networks, Computer, Thyroid Nodule, Young Adult
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