Classification of thyroid nodules based on analysis of margin characteristic

Nugroho, Hanung Adi; Frannita, Eka Legya; Nugroho, Anan; Zulfanahri,; Ardiyanto, Igi; Choridah, Lina

doi:10.1109/ic3ina.2017.8251738

Cited by 13 publications

(8 citation statements)

References 17 publications

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“…Singh and Jindal [8] first extracted 13 gray-level cooccurrence matrix (GLCM) features and then utilized a support vector machine (SVM) to classify thyroid nodules with a maximum classification accuracy of 84.62%. Nugroho et al [9] classified thyroid nodules by analyzing the edge features of nodules in ultrasound images with a final accuracy of 92.30%. Iakovidis et al [10] used local binary patterns (LBPs), fuzzy local binary patterns (FLBPs), and fuzzy gray-level histograms (FGLHs) to train SVMs with polynomial kernels to detect thyroid nodules.…”

Section: Introductionmentioning

confidence: 99%

Cascade and Fusion of Multitask Convolutional Neural Networks for Detection of Thyroid Nodules in Contrast-Enhanced CT

Zhen

et al. 2019

Computational Intelligence and Neuroscience

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With the development of computed tomography (CT), the contrast-enhanced CT scan is widely used in the diagnosis of thyroid nodules. However, due to the artifacts and high complexity of thyroid CT images, traditional machine learning has difficulty in detecting thyroid nodules in contrast-enhanced CT. A fully automated detection algorithm for thyroid nodules using contrast-enhanced CT images is developed. A modified U-Net architecture of fully convolutional networks is employed to segment the thyroid region of interest (ROI), and a fusion of convolutional neural networks (CNN-Fs) is proposed to detect benign and malignant thyroid nodules from the ROI images and original contrast-enhanced CT images. Experimental results demonstrate that the proposed cascade and fusion method of multitask convolutional neural networks (CNNs) is efficient in diagnosing thyroid diseases with contrast-enhanced CT images and has superior performance compared with other CNN methods.

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Section: Introductionmentioning

confidence: 99%

Cascade and Fusion of Multitask Convolutional Neural Networks for Detection of Thyroid Nodules in Contrast-Enhanced CT

Zhen

et al. 2019

Computational Intelligence and Neuroscience

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“…A large amount indicates a sharp change on the margin at the given coordinates. 7 The algorithm also calculates the angle θ : p n − 1 → p n → p n + 1 , where a large angle indicates a slow change and a small angle indicates a sharp change at the given coordinates. The κ and θ values are then combined to estimate the angulation using the rule f A in equation (2):…”

Section: Methodsmentioning

confidence: 99%

“…Our irregularity method and the methods developed in 5,7,12,14 both measure global irregularity of the nodule, but our method uses convexity and ellipticity variance, providing a more robust and accurate assessment of nodule irregularity without being excessive. Furthermore, our method has a new feature extraction step that incorporates and measures of local irregularity of margin such as lobulation and angulation.…”

Section: Methodsmentioning

confidence: 99%

Automatic Detection of Thyroid Nodule Characteristics From 2D Ultrasound Images

Han,

Ibrahim,

et al. 2023

Ultrason Imaging

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Thyroid cancer is one of the common types of cancer worldwide, and Ultrasound (US) imaging is a modality normally used for thyroid cancer diagnostics. The American College of Radiology Thyroid Imaging Reporting and Data System (ACR TIRADS) has been widely adopted to identify and classify US image characteristics for thyroid nodules. This paper presents novel methods for detecting the characteristic descriptors derived from TIRADS. Our methods return descriptions of the nodule margin irregularity, margin smoothness, calcification as well as shape and echogenicity using conventional computer vision and deep learning techniques. We evaluate our methods using datasets of 471 US images of thyroid nodules acquired from US machines of different makes and labeled by multiple radiologists. The proposed methods achieved overall accuracies of 88.00%, 93.18%, and 89.13% in classifying nodule calcification, margin irregularity, and margin smoothness respectively. Further tests with limited data also show a promising overall accuracy of 90.60% for echogenicity and 100.00% for nodule shape. This study provides an automated annotation of thyroid nodule characteristics from 2D ultrasound images. The experimental results showed promising performance of our methods for thyroid nodule analysis. The automatic detection of correct characteristics not only offers supporting evidence for diagnosis, but also generates patient reports rapidly, thereby decreasing the workload of radiologists and enhancing productivity.

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“…The results show that the SVM has better accuracy than K-NN and Bayesian methods. Nugroho et al [2] classified thyroid nodules by analyzing the edge features of nodules in ultrasound images. Xiang Ying et al [3] proposed a cascaded convolutional neural network to segment thyroid nodules in ultrasound images based on UNet, CNN, and FCN.…”

Section: Introductionmentioning

confidence: 99%

Research on thyroid CT image segmentation based on U-shaped convolutional neural network

Zeng¹,

Zhang²,

Gong³

et al. 2023

Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022)

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Computer-aided thyroid CT image segmentation aims to provide imaging physicians and clinicians with auxiliary diagnostic suggestions and improve the efficiency of physicians in diagnosing the thyroid region. However, it is still a challenging task to distinguish the thyroid from other surrounding tissues due to adhesions in thyroid CT images caused by thyroid disease. To achieve accurate segmentation of thyroid CT images under the intervention of different types of thyroid nodules, we proposed a thyroid segmentation network named ResUnet by introducing the residual learning idea to UNet. Our network controls the gradient dispersion by incorporating a batch normalization operation and an intermediate layer regularization operation, then solves the degradation problem by introducing the residual connections into the convolution operation. Moreover, our ResUnet network can converge faster with the same number of layers, thus supporting a deeper design of the network. Extensive experiments also validated the high accuracy (94.10%), specificity (98.94%), and sensitivity (96.34%) of the proposed ResUnet for the segmentation of thyroid nodules, which can assist CT physicians in the diagnosis of the thyroid gland.

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Classification of thyroid nodules based on analysis of margin characteristic

Cited by 13 publications

References 17 publications

Cascade and Fusion of Multitask Convolutional Neural Networks for Detection of Thyroid Nodules in Contrast-Enhanced CT

Cascade and Fusion of Multitask Convolutional Neural Networks for Detection of Thyroid Nodules in Contrast-Enhanced CT

Automatic Detection of Thyroid Nodule Characteristics From 2D Ultrasound Images

Research on thyroid CT image segmentation based on U-shaped convolutional neural network

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