Ultrasound image-based thyroid nodule automatic segmentation using convolutional neural networks

Ma, Jinlian; Wu, Fuyong; Jiang, Tianan; Zhao, Qiyu; Kong, Dexing

doi:10.1007/s11548-017-1649-7

Cited by 144 publications

(72 citation statements)

References 22 publications

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“…For the thyroid nodule diagnosis problem, Ma et al proposed a cascade deep convolutional neural network (CNNs)‐based model for ultrasound thyroid nodule diagnosis. The proposed model consists of the fusion of two different CNNs and a new splitting method.…”

Section: Introductionsupporting

confidence: 91%

Thyroid nodules classification and diagnosis in ultrasound images using fine‐tuning deep convolutional neural network

Moussa

Khachnaoui

Guetari

et al. 2019

Int J Imaging Syst Tech

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Ultrasonography AKA diagnostic sonography is a noninvasive imaging technique that allows the analysis of an organic structure, thanks to the ultrasonic waves. It is a valuable diagnosis method and is also seen as the evidence‐based diagnostic method for thyroid nodules. The diagnosis, however, is visually made by the practitioner. The automatic discrimination of benign and malignant nodules would be very useful to report Thyroid Imaging Reporting. In this paper, we propose a fine‐tuning approach based on deep learning using a Convolutional Neural Network model named resNet‐50. This approach allows improving the effectiveness of the classification of thyroid nodules in ultrasound images. Experiments have been conducted on 814 ultrasound images and the results show that our proposed approach dramatically improves the accuracy of the classification of thyroid nodules and outperforms The VGG‐19 model.

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Section: Introductionsupporting

confidence: 91%

Thyroid nodules classification and diagnosis in ultrasound images using fine‐tuning deep convolutional neural network

Moussa

Khachnaoui

Guetari

et al. 2019

Int J Imaging Syst Tech

View full text Add to dashboard Cite

show abstract

“…However, for thyroid nodules with a complex background, the accuracy of deep learning segmentation should be optimized by expanding the data size and adding training layers. [41] In general, quantitative metrics, namely, Dice coefficient, Jaccard coefficient, Boundary displacement error, and global consistency error are adopted to validate the segmentation. [42,43] A quantitative analysis is convenient for the horizontal comparison of segmentation efficiency using different algorithm methods.…”

Section: Image Classification Techniques In Thyroid Ultrasoundmentioning

confidence: 99%

Update on thyroid ultrasound

Liang

Cai

et al. 2019

Chinese Medical Journal

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Objective Ultrasound imaging is well known to play an important role in the detection of thyroid disease, but the management of thyroid ultrasound remains inconsistent. Both standardized diagnostic criteria and new ultrasound technologies are essential for improving the accuracy of thyroid ultrasound. This study reviewed the global guidelines of thyroid ultrasound and analyzed their common characteristics for basic clinical screening. Advances in the application of a combination of thyroid ultrasound and artificial intelligence (AI) were also presented. Data sources An extensive search of the PubMed database was undertaken, focusing on research published after 2001 with keywords including thyroid ultrasound, guideline, AI, segmentation, image classification, and deep learning. Study selection Several types of articles, including original studies and literature reviews, were identified and reviewed to summarize the importance of standardization and new technology in thyroid ultrasound diagnosis. Results Ultrasound has become an important diagnostic technique in thyroid nodules. Both standardized diagnostic criteria and new ultrasound technologies are essential for improving the accuracy of thyroid ultrasound. In the standardization, since there are no global consensus exists, common characteristics such as a multi-feature diagnosis, the performance of lymph nodes, explicit indications of fine needle aspiration, and the diagnosis of special populations should be focused on. Besides, evidence suggests that AI technique has a good effect on the unavoidable limitations of traditional ultrasound, and the combination of diagnostic criteria and AI may lead to a great promotion in thyroid diagnosis. Conclusion Standardization and development of novel techniques are key factors to improving thyroid ultrasound, and both should be considered in normal clinical use.

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“…Ma et al [12, 13] proposed a hybrid method to classify thyroid nodules, which is a fusion of two pretrained CNNs. Ma et al [14] also employed a deep CNN to automatically segment thyroid nodules from ultrasound images. The experimental results demonstrate the potential clinical application of the new method, but a clinical application has not been performed.…”

Section: Introductionmentioning

confidence: 99%

Automatic thyroid nodule recognition and diagnosis in ultrasound imaging with the YOLOv2 neural network

et al. 2019

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BackgroundIn this study, images of 2450 benign thyroid nodules and 2557 malignant thyroid nodules were collected and labeled, and an automatic image recognition and diagnosis system was established by deep learning using the YOLOv2 neural network. The performance of the system in the diagnosis of thyroid nodules was evaluated, and the application value of artificial intelligence in clinical practice was investigated.MethodsThe ultrasound images of 276 patients were retrospectively selected. The diagnoses of the radiologists were determined according to the Thyroid Imaging Reporting and Data System; the images were automatically recognized and diagnosed by the established artificial intelligence system. Pathological diagnosis was the gold standard for the final diagnosis. The performances of the established system and the radiologists in diagnosing the benign and malignant thyroid nodules were compared.ResultsThe artificial intelligence diagnosis system correctly identified the lesion area, with an area under the receiver operating characteristic (ROC) curve of 0.902, which is higher than that of the radiologists (0.859). This finding indicates a higher diagnostic accuracy (p = 0.0434). The sensitivity, positive predictive value, negative predictive value, and accuracy of the artificial intelligence diagnosis system for the diagnosis of malignant thyroid nodules were 90.5%, 95.22%, 80.99%, and 90.31%, respectively, and the performance did not significantly differ from that of the radiologists (p > 0.05). The artificial intelligence diagnosis system had a higher specificity (89.91% vs 77.98%, p = 0.026).ConclusionsCompared with the performance of experienced radiologists, the artificial intelligence system has comparable sensitivity and accuracy for the diagnosis of malignant thyroid nodules and better diagnostic ability for benign thyroid nodules. As an auxiliary tool, this artificial intelligence diagnosis system can provide radiologists with sufficient assistance in the diagnosis of benign and malignant thyroid nodules.

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Ultrasound image-based thyroid nodule automatic segmentation using convolutional neural networks

Cited by 144 publications

References 22 publications

Thyroid nodules classification and diagnosis in ultrasound images using fine‐tuning deep convolutional neural network

Thyroid nodules classification and diagnosis in ultrasound images using fine‐tuning deep convolutional neural network

Update on thyroid ultrasound

Automatic thyroid nodule recognition and diagnosis in ultrasound imaging with the YOLOv2 neural network

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