Cited by 143AI in cancer detectionMedical Image Segmentation TechniquesThyroid Cancer Diagnosis and Treatment

Cascade convolutional neural networks for automatic detection of thyroid nodules in ultrasound images

Ma, Jinlian, Wu, Fa, Jiang, Tian'an, Zhu, Jiang, Kong, Dexing·2017·Medical Physics

Abstract

PURPOSE: It is very important for calculation of clinical indices and diagnosis to detect thyroid nodules from ultrasound images. However, this task is a challenge mainly due to heterogeneous thyroid nodules with distinct components are similar to background in ultrasound images. In this study, we employ cascade deep convolutional neural networks (CNNs) to develop and evaluate a fully automatic detection of thyroid nodules from 2D ultrasound images. METHODS: Our cascade CNNs are a type of hybrid model, consisting of two different CNNs and a new splitting method. Specifically, it employs a deep CNN to learn the segmentation probability maps from the ground true data. Then, all the segmentation probability maps are split into different connected regions by the splitting method. Finally, another deep CNN is used to automatically detect the thyroid nodules from ultrasound thyroid images. RESULTS: Experiment results illustrate the cascade CNNs are very effective in detection of thyroid nodules. Specially, the value of area under the curve of receiver operating characteristic is 98.51%. The Free-response receiver operating characteristic (FROC) and jackknife alternative FROC (JAFROC) analyses show a significant improvement in the performance of our cascade CNNs compared to that of other methods. The multi-view strategy can improve the performance of cascade CNNs. Moreover, our special splitting method can effectively separate different connected regions so that the second CNN can correctively gain the positive and negative samples according to the automatic labels. CONCLUSIONS: The experiment results demonstrate the potential clinical applications of this proposed method. This technique can offer physicians an objective second opinion, and reduce their heavy workload so as to avoid misdiagnosis causes because of excessive fatigue. In addition, it is easy and reproducible for a person without medical expertise to diagnose thyroid nodules.

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Crossref · 10.1002/mp.12134

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  • CNN is one of the most popular deep architectures and has gained great success in image classification, object detection, and target segmentation.

    “CNN is known as one of the most popular deep architectures and has also gained great success in various tasks, such as image classification [48,49], object detection [29,30], and target segmentation [44,50].”

    From Deep Learning in Medical Ultrasound Analysis: A Review (2019) · cites this paper as [29]

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Authors
Ma, J., Wu, F., Jiang, T., Zhu, J., & Kong, D.
Affiliations
  • Zhejiang University (CN)
  • Department of Ultrasound First Affiliated Hospital Zhejiang University Hangzhou 310003 China
  • Sir Run Run Shaw Hospital (CN)
  • School of Mathematical Sciences Zhejiang University Hangzhou 310027 China
Venue
Medical Physics
Publisher
Wiley
Published
2017-04-17
Volume
44
Issue
5
Pages
1678-1691
ISSN
  • 0094-2405 (ISSN-L)
  • 2473-4209
OpenAlex
W2586932530
Cited by
142
References
40
Citations by year
2026: 5 · 2025: 12 · 2024: 9 · 2023: 25 · 2022: 17 · 2021: 25 · 2020: 21 · 2019: 19 · 2018: 8 · 2017: 1
FWCI
7.99
Type
journal article
Language
en
Open access
closed
Locations
  • Medical Physics (publishedVersion)
  • PubMed (publishedVersion)
Funders
National Natural Science Foundation of China (LZ13A010002)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Thyroid Cancer Diagnosis and Treatment
Keywords
thyroid nodule detection, thyroid ultrasound, cascade convolutional neural network, automatic image labeling, computer-aided diagnosis
MeSH terms
Humans, Probability, ROC Curve, Ultrasonography, Neural Networks, Computer, Thyroid Nodule

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