Thyroid Nodules In Ultrasound Images Research Articles

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

AbstractUltrasonography 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.

Diagnosis of Thyroid Nodules Based on Local Non-quantitative Multi-Directional Texture Descriptor with Rotation Invariant Characteristics for Ultrasound Image.

The traditional texture feature lacks the directional analysis of graphical element, so it could not better distinguish the thyroid nodule texture image formed by the rotation of graphical element. A non-quantifiable local feature is adopted in this paper to design a robust texture descriptor so as to enhance the robustness of the texture classification in the rotation and scale changes, which can improve the diagnostic accuracy of thyroid nodules in ultrasound images. First of all, the concept of local feature with rotational symmetry is introduced. It is found that many rotation invariant local features are rotational symmetric to a certain degree. Therefore, we propose a novel local feature to describe the rotation invariant properties of the texture. In order to deal with the change of rotation and scale of ultrasound thyroid nodules in image, Pairwise rotation-invariant spatial context feature is adopted to analyze the texture feature, which can combine with the scale information without increasing the dimension of the local feature. The fadopted local features have strong robustness to rotation and gray intensity variation. The experimental results show that our proposed method outperforms the existing algorithms on thyroid ultrasound data sets, which greatly improve the Diagnosis accuracy of thyroid nodules.

Classification of Thyroid Nodules in Ultrasound Images Using Direction-Independent Features Extracted by Two-Threshold Binary Decomposition.

In recent years, several computer-aided diagnosis systems emerged for the diagnosis of thyroid gland disorders using ultrasound imaging. These systems based on machine learning algorithms may offer a second opinion to radiologists by evaluating a malignancy risk of thyroid tissue, thus increasing the overall diagnostic accuracy of ultrasound imaging. Although current computer-aided diagnosis systems exhibit promising results, their use in clinical practice is limited. One of the main limitations is that the majority of them use direction-dependent features. Our intention has been to design a computer-aided diagnosis system, which will use only direction-independent features, that is, it will not be dependent on the orientation and the inclination angle of the ultrasound probe when acquiring the image. We have, therefore, applied histogram analysis and segmentation-based fractal texture analysis algorithm, which calculates direction-independent features only. In our study, 40 thyroid nodules (20 malignant and 20 benign) were used to extract several features, such as histogram parameters, fractal dimension, and mean brightness value in different grayscale bands (obtained by 2-threshold binary decomposition). The features were then used in support vector machine and random forests classifiers to differentiate nodules into malignant and benign classes. Using leave-one-out cross-validation method, the overall accuracy was 92.42% for random forests and 94.64% for support vector machine. Results show that both methods are useful in practice; however, support vector machine provides better results for this application. Proposed computer-aided diagnosis system can provide support to radiologists in their current diagnosis of thyroid nodules, whereby it can optimize the overall accuracy of ultrasound imaging.

Patch-based classification of thyroid nodules in ultrasound images using direction independent features extracted by two-threshold binary decomposition

Ultrasound imaging of the thyroid gland is considered to be the best diagnostic choice for evaluating thyroid nodules in early stages, since it has been marked as cost-effective, non-invasive and risk-free. Computer aided diagnosis (CAD) systems can offer a second opinion to radiologists, thereby increasing the overall diagnostic accuracy of ultrasound imaging. Although current CAD systems exhibit promising results, their use in clinical practice is limited. Some of the main limitations are that the majority use direction dependent features so, they are only compatible with static images in just one plane (axial or longitudinal), requiring precise segmentation of a nodule. Our intention has been to design a CAD system which will use only direction independent features i.e., not dependent upon the orientation or inclination angle of the ultrasound probe when acquiring the image. In this study, 60 thyroid nodules (20 malignant, 40 benign) were divided into small patches of 17 × 17 pixels, which were then used to extract several direction independent features by employing Two-Threshold Binary Decomposition, a method that decomposes an image into the set of binary images. The features were then used in Random Forests (RF) and Support Vector Machine (SVM) classifiers to categorize nodules into malignant and benign classes. Classification was evaluated using group 10-fold cross-validation method. Performance on individual patches was then averaged to classify whole nodules with the following results: overall accuracy, sensitivity, specificity and area under receiver operating characteristics (ROC) curve: 95%, 95%, 95%, 0.971 for RF and; 91.6%, 95%, 90%, 0.965 for SVM respectively. The patch-based CAD system we present can provide support to radiologists in their current diagnosis of thyroid nodules, whereby it can increase the overall accuracy of ultrasound imaging.

Automated Analysis of Gray-Scale Ultrasound Images of Thyroid Nodules (“Radiomics”) May Outperform Image Interpretation by Less Experienced Thyroid Radiologists

Automated Analysis of Gray-Scale Ultrasound Images of Thyroid Nodules (“Radiomics”) May Outperform Image Interpretation by Less Experienced Thyroid Radiologists

Web-based thyroid imaging reporting and data system: Malignancy risk of atypia of undetermined significance or follicular lesion of undetermined significance thyroid nodules calculated by a combination of ultrasonography features and biopsy results.

The purpose of this study was to construct a web-based predictive model using ultrasound characteristics and subcategorized biopsy results for thyroid nodules of atypia of undetermined significance/follicular lesion of undetermined significance (AUS/FLUS) to stratify the risk of malignancy. Data included 672 thyroid nodules from 656 patients from a historical cohort. We analyzed ultrasound images of thyroid nodules and biopsy results according to nuclear atypia and architectural atypia. Multivariate logistic regression analysis was performed to predict whether nodules were diagnosed as malignant or benign. The ultrasound features, including spiculated margin, marked hypoechogenicity, calcifications, biopsy results, and cytologic atypia, showed significant differences between groups. A 13-point risk scoring system was developed, and the area under the curve (AUC) of the receiver operating characteristic (ROC) curve of the development and validation sets were 0.837 and 0.830, respectively (http://www.gap.kr/thyroidnodule_b3.php). We devised a web-based predictive model using the combined information of ultrasound characteristics and biopsy results for AUS/FLUS thyroid nodules to stratify the malignant risk.

Optimized multi-level elongated quinary patterns for the assessment of thyroid nodules in ultrasound images

Ultrasound imaging is one of the most common visualizing tools used by radiologists to identify the location of thyroid nodules. However, visual assessment of nodules is difficult and often affected by inter- and intra-observer variabilities. Thus, a computer-aided diagnosis (CAD) system can be helpful to cross-verify the severity of nodules. This paper proposes a new CAD system to characterize thyroid nodules using optimized multi-level elongated quinary patterns. In this study, higher order spectral (HOS) entropy features extracted from these patterns appropriately distinguished benign and malignant nodules under particle swarm optimization (PSO) and support vector machine (SVM) frameworks. Our CAD algorithm achieved a maximum accuracy of 97.71% and 97.01% in private and public datasets respectively. The evaluation of this CAD system on both private and public datasets confirmed its effectiveness as a secondary tool in assisting radiological findings.

Interobserver agreement of various thyroid imaging reporting and data systems.

Ultrasonography is the best available tool for the initial work-up of thyroid nodules. Substantial interobserver variability has been documented in the recognition and reporting of some of the lesion characteristics. A number of classification systems have been developed to estimate the likelihood of malignancy: several of them have been endorsed by scientific societies, but their reproducibility is yet to be assessed. We evaluated the interobserver variability of the AACE/ACE/AME, ACR, ATA, EU-TIRADS and K-TIRADS classification systems and the interobserver concordance in the indication to FNA biopsy. Two raters independently evaluated 1055 ultrasound images of thyroid nodules identified in 265 patients at multiple time points, in two separate sets (501 and 554 images). After the first set of nodules, a joint reading was performed to reach a consensus in the feature definitions. The interobserver agreement (Krippendorff alpha) in the first set of nodules was 0.47, 0.49, 0.49, 0.61 and 0.53, for AACE/ACE/AME, ACR, ATA, EU-TIRADS and K-TIRADS systems, respectively. The agreement for the indication to biopsy was substantial to near-perfect, being 0.73, 0.61, 0.75, 0.68 and 0.82, respectively (Cohen’s kappa). For all systems, agreement on the nodules of the second set increased. Despite the wide variability in the description of single ultrasonographic features, the classification systems may improve the interobserver agreement that further ameliorates after a specific training. When selecting nodules to be submitted to FNA biopsy, that is main purpose of these classifications, the interobserver agreement is substantial to almost perfect.

Thyroid Nodule Classification in Ultrasound Images by Fine-Tuning Deep Convolutional Neural Network

With many thyroid nodules being incidentally detected, it is important to identify as many malignant nodules as possible while excluding those that are highly likely to be benign from fine needle aspiration (FNA) biopsies or surgeries. This paper presents a computer-aided diagnosis (CAD) system for classifying thyroid nodules in ultrasound images. We use deep learning approach to extract features from thyroid ultrasound images. Ultrasound images are pre-processed to calibrate their scale and remove the artifacts. A pre-trained GoogLeNet model is then fine-tuned using the pre-processed image samples which leads to superior feature extraction. The extracted features of the thyroid ultrasound images are sent to a Cost-sensitive Random Forest classifier to classify the images into “malignant” and “benign” cases. The experimental results show the proposed fine-tuned GoogLeNet model achieves excellent classification performance, attaining 98.29% classification accuracy, 99.10% sensitivity and 93.90% specificity for the images in an open access database (Pedraza et al. 16), while 96.34% classification accuracy, 86% sensitivity and 99% specificity for the images in our local health region database.

Detection and classification of thyroid nodule using Shearlet coefficients and support vector machine

Thyroid nodules have diversified internal components and dissimilar echo patterns in ultrasound images. Textural features are used to characterize these echo patterns. This paper presents a classification scheme that uses shearlet transform based textural features for the classification of thyroid nodules in ultrasound images. The study comprised of 60 thyroid ultrasound images (30 with benign nodules and 30 with malignant nodules). Total of 22 features are extracted. Support vector machine (SVM) and K nearest neighbor (KNN) are used to differentiate benign and malignant nodules. The diagnostic sensitivity, specificity, F1_score and accuracy of both the classifiers are calculated. A comparative study has been carried out with respect to their performances. The sensitivity of SVM with radial basis function (RBF) kernel is 100% as compared to that of KNN with 96.33%. The proposed features can increase the accuracy of the classifier and decrease the rate of misdiagnosis in thyroid nodule classification.

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

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. 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. 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. 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.

Automated delineation of thyroid nodules in ultrasound images using spatial neutrosophic clustering and level set

An accurate contour estimation plays a significant role in classification and estimation of shape, size, and position of thyroid nodule. This helps to reduce the number of false positives, improves the accurate detection and efficient diagnosis of thyroid nodules. This paper introduces an automated delineation method that integrates spatial information with neutrosophic clustering and level-sets for accurate and effective segmentation of thyroid nodules in ultrasound images. The proposed delineation method named as Spatial Neutrosophic Distance Regularized Level Set (SNDRLS) is based on Neutrosophic L-Means (NLM) clustering which incorporates spatial information for Level Set evolution. The SNDRLS takes rough estimation of region of interest (ROI) as input provided by Spatial NLM (SNLM) clustering for precise delineation of one or more nodules. The performance of the proposed method is compared with level set, NLM clustering, Active Contour Without Edges (ACWE), Fuzzy C-Means (FCM) clustering and Neutrosophic based Watershed segmentation methods using the same image dataset. To validate the SNDRLS method, the manual demarcations from three expert radiologists are employed as ground truth. The SNDRLS yields the closest boundaries to the ground truth compared to other methods as revealed by six assessment measures (true positive rate is 95.45±3.5%, false positive rate is 7.32±5.3% and overlap is 93.15±5. 2%, mean absolute distance is 1.8±1.4 pixels, Hausdorff distance is 0.7±0.4 pixels and Dice metric is 94.25±4.6%). The experimental results show that the SNDRLS is able to delineate multiple nodules in thyroid ultrasound images accurately and effectively. The proposed method achieves the automated nodule boundary even for low-contrast, blurred, and noisy thyroid ultrasound images without any human intervention. Additionally, the SNDRLS has the ability to determine the controlling parameters adaptively from SNLM clustering.

A segmentation method and classification of diagnosis for thyroid nodules

Heterogeneous features of thyroid nodules in ultrasound images is very difficult task when radiologists and physicians manually draw a complete shape of nodule, size and shape, image or distinguish what type of nodule is exist. Segmentation and classification is important methods for medical image processing. Ultrasound imaging is the best way to prediction of which type of thyroid is there. In this paper, uses the groups Benign (non-cancerous) and Malignant (cancerous) Thyroid Nodules images were used. The texture feature method like GLCM are very useful for classifying texture of images and these features are used to train the classifiers such as SVM, KNN and Bayesian. The experimental result shows the performance of the classifiers and shows the best predictive value and positively identify the percentage of the non-cancerous or cancerous people and shows the best performance accuracy using the SVM classifier as compare to the KNN and Bayesian classifier.

Classification of the Thyroid Nodules Based on Characteristic Sonographic Textural Feature and Correlated Histopathology Using Hierarchical Support Vector Machines

In this study, the ultrasound images of thyroid nodules were classified to facilitate clinical diagnosis and management. The hierarchical support vector machines (SVM) classification system was used to select the characteristic sonographic textural feature that represents the major histopathologic components of the thyroid nodules. Two ultrasound systems (LA39 and i12L mentioned in the Materials and Methods section) were used for comparison. Seventy-six thyroid nodular lesions and 157 regions-of-interest thyroid ultrasound image from each system were recruited in the study. The parameters affecting image acquisition were kept in the same condition for all lesions. Commonly used texture analysis methods were applied to characterize thyroid ultrasound images. Image features were classified according to the corresponding pathologic findings. To estimate their relevance and performance to classification, SVMs were used as a feature selector and a classifier. The thyroid nodules are first categorized as two main types, i.e., follicle base and fibrosis base nodule, by sum average. The follicle base nodules can be further and completely classified into follicles with few cells, follicles with follicular cells and follicles with papillary cancer cells by run length nonuniformity (RLNU). The fibrosis base nodules are further classified by sum square into fibrosis with few cells and fibrosis with dominant cells. The fibrosis base neoplasm with dominant cells can be separated into fibrosis with follicular cells and fibrosis with papillary cancer cells by entropy. The hierarchical SVM classification system achieves a diagnostic accuracy between 96.34% and 100%. Besides, the best sonographic textural feature can be selected by the system for the differentiation between the follicle and fibrosis base thyroid nodules or the cell types mixed in them. In follicle base thyroid nodules, papillary cancers show higher sonographic textural RLNU but less than follicular cells. In fibrosis base thyroid nodules, papillary cancers show increased sonographic textural variance and entropy. (E-mail: a120930@tzuchi.com.tw)

Application of support-vector-machine-based method for feature selection and classification of thyroid nodules in ultrasound images

Most thyroid nodules are heterogeneous with various internal components, which confuse many radiologists and physicians with their various echo patterns in ultrasound images. Numerous textural feature extraction methods are used to characterize these patterns to reduce the misdiagnosis rate. Thyroid nodules can be classified using the corresponding textural features. In this paper, six support vector machines (SVMs) are adopted to select significant textural features and to classify the nodular lesions of a thyroid. Experiment results show that the proposed method can correctly and efficiently classify thyroid nodules. A comparison with existing methods shows that the feature-selection capability of the proposed method is similar to that of the sequential-floating-forward-selection (SFFS) method, while the execution time is about 3–37 times faster. In addition, the proposed criterion function achieves higher accuracy than those of the F-score, T-test, entropy, and Bhattacharyya distance methods.

A computer-aided system for malignancy risk assessment of nodules in thyroid US images based on boundary features

In this paper, a novel computer-based approach is proposed for malignancy risk assessment of thyroid nodules in ultrasound images. The proposed approach is based on boundary features and is motivated by the correlation which has been addressed in medical literature between nodule boundary irregularity and malignancy risk. In addition, local echogenicity variance is utilized so as to incorporate information associated with local echogenicity distribution within nodule boundary neighborhood. Such information is valuable for the discrimination of high-risk nodules with blurred boundaries from medium-risk nodules with regular boundaries. Analysis of variance is performed, indicating that each boundary feature under study provides statistically significant information for the discrimination of thyroid nodules in ultrasound images, in terms of malignancy risk. k-nearest neighbor and support vector machine classifiers are employed for the classification tasks, utilizing feature vectors derived from all combinations of features under study. The classification results are evaluated with the use of the receiver operating characteristic. It is derived that the proposed approach is capable of discriminating between medium-risk and high-risk nodules, obtaining an area under curve, which reaches 0.95.

Active Contours Guided by Echogenicity and Texture for Delineation of Thyroid Nodules in Ultrasound Images

Thyroid nodules are solid or cystic lumps formed in the thyroid gland and may be caused by a variety of thyroid disorders. This paper presents a novel active contour model for precise delineation of thyroid nodules of various shapes according to their echogenicity and texture, as displayed in ultrasound (US) images. The proposed model, named joint echogenicity-texture (JET), is based on a modified Mumford-Shah functional that, in addition to regional image intensity, incorporates statistical texture information encoded by feature distributions. The distributions are aggregated within the functional through new log-likelihood goodness-of-fit terms. The JET model requires only a rough region of interest within the thyroid gland as input and automatically proceeds with precise delineation of the nodules, revealing their shape and size. The performance of the JET model was validated on a range of US images displaying hypoechoic and isoechoic nodules of various shapes. The quantification of the results shows that the JET model: 1) provides precise delineations of thyroid nodules as compared to "ground truth" delineations obtained by experts and 2) copes with the limitations of the previous thyroid US delineation approaches as it is capable of delineating thyroid nodules regardless of their echogenicity or shape.