scholarly journals Automatic Recognition and Classification System of Thyroid Nodules in CT Images Based on CNN

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Wenjun Li ◽  
Siyi Cheng ◽  
Kai Qian ◽  
Keqiang Yue ◽  
Hao Liu
Keyword(s):  
Image Processing ◽  
Thyroid Nodule ◽  
Classification System ◽  
Thyroid Nodules ◽  
Feature Fusion ◽  
Automatic Recognition ◽  
Thyroid Diseases ◽  
Ct Image ◽  
Image Processing Algorithm ◽  
End To End

Thyroid nodule lesions are one of the most common lesions of the thyroid; the incidence rate has been the highest in the past thirty years. X-ray computed tomography (CT) plays an increasingly important role in the diagnosis of thyroid diseases. Nonetheless, as a result of the artifact and high complexity of thyroid CT image, the traditional machine learning method cannot be applied to CT image processing. In this paper, an end-to-end thyroid nodule automatic recognition and classification system is designed based on CNN. An improved Eff-Unet segmentation network is used to segment thyroid nodules as ROI. The image processing algorithm optimizes the ROI region and divides the nodules. A low-level and high-level feature fusion classification network CNN-F is proposed to classify the benign and malignant nodules. After each module is connected in series with the algorithm, the automatic classification of each nodule can be realized. Experimental results demonstrate that the proposed end-to-end thyroid nodule automatic recognition and classification system has excellent performance in diagnosing thyroid diseases. In the test set, the segmentation IOU reaches 0.855, and the classification output accuracy reaches 85.92%.

scholarly journals Role of Ultrasonography in Thyroid Nodules with Pathological Correlation

2020 ◽  
Vol 8 (1) ◽  
pp. 100-105
Author(s):  
Subhash C. Sylonia ◽  
Pranay Kumar
Keyword(s):  
Thyroid Nodule ◽  
Sensitivity And Specificity ◽  
Thyroid Nodules ◽  
Imaging Modality ◽  
High Sensitivity ◽  
Thyroid Diseases ◽  
Imaging Features ◽  
Medical Sciences ◽  
Ultrasound Evaluation ◽  
Ultrasound Findings

Background: Thyroid nodule imaging had been a challenging task. Ultrasound has been the modality of choice for detection & evaluation of these lesions. This study was done to identify the accuracy of various imaging features in thyroid nodules that are associated with benignity  and malignancy and the overall accuracy of ultrasound in determining malignant nodules. The objective of the study are: (1) Ultrasonographic evaluation of thyroid to characterize the thyroid disease and nodules (benign vs malignant) and their morphologic evaluation. (2) Sensitivity and Specificity on the basis of radiological, and cytopathological correlation for suspected thyroid lesions. Subjects and Methods: A total of seventy patients (70) of both sexes and different age group, meeting the inclusion criterion, were the part of present prospective study. The cases referred to the Department of Radio-diagnosis and imaging from OPD/IPD of Saraswathi Institute of Medical sciences, Hapur from October’2017 to July’2019. An informed consent was taken from all the patients subjected for evaluation. Results: The collective data was analysed for Ultrasound findings of thyroid nodule which presented clinically as solitary or multiple thyroid nodules. The total number of cases analysed was 70 out of which 48 were found to have solitary/multinodular lesions clincally. On ultrasound evaluation out of 48 patients 38   had solitary/multinodular lesions. Conclusion: High resolution sonography is recommended as the primary imaging modality in evaluation of thyroid diseases. It has a high sensitivity and specificity in the diagnosis of thyroid diseases.

High-resolution thyroid nodule clinic: analysis of 1674 thyroid nodules

2014 ◽  
Author(s):  
Jose Carlos Fernandez-Garcia ◽  
Carmen Maria Cortes-Salazar ◽  
Isabel Mancha-Doblas ◽  
Francisco Tinahones
Keyword(s):  
High Resolution ◽  
Thyroid Nodule ◽  
Thyroid Nodules

Estimating OLED Display Device Lifetime from pixel and screen brightness and its application

2019 ◽  
Vol 2019 (1) ◽  
pp. 331-338 ◽  
Author(s):  
Jérémie Gerhardt ◽  
Michael E. Miller ◽  
Hyunjin Yoo ◽  
Tara Akhavan
Keyword(s):  
Image Processing ◽  
Power Consumption ◽  
Model Parameters ◽  
Display Device ◽  
Image Processing Algorithm ◽  
Pixel Value ◽  
Device Lifetime ◽  
The Impact ◽  
Oled Display

In this paper we discuss a model to estimate the power consumption and lifetime (LT) of an OLED display based on its pixel value and the brightness setting of the screen (scbr). This model is used to illustrate the effect of OLED aging on display color characteristics. Model parameters are based on power consumption measurement of a given display for a number of pixel and scbr combinations. OLED LT is often given for the most stressful display operating situation, i.e. white image at maximum scbr, but having the ability to predict the LT for other configurations can be meaningful to estimate the impact and quality of new image processing algorithms. After explaining our model we present a use case to illustrate how we use it to evaluate the impact of an image processing algorithm for brightness adaptation.

scholarly journals Choosing the best algorithm among five thyroid nodule ultrasound scores: from performance to cytology sparing—a single-center retrospective study in a large cohort

2021 ◽  
Author(s):  
Clotilde Sparano ◽  
Valentina Verdiani ◽  
Cinzia Pupilli ◽  
Giuliano Perigli ◽  
Benedetta Badii ◽  
...  
Keyword(s):  
Thyroid Nodule ◽  
Thyroid Nodules ◽  
Real Life ◽  
Scoring Systems ◽  
Diagnostic Procedures ◽  
Needle Aspiration ◽  
Common Disease ◽  
Single Center ◽  
Thyroid Microcarcinoma ◽  
Thyroid Malignancies

Abstract Objective Incidental diagnosis of thyroid nodules, and therefore of thyroid cancer, has definitely increased in recent years, but the mortality rate for thyroid malignancies remains very low. Within this landscape of overdiagnosis, several nodule ultrasound scores (NUS) have been proposed to reduce unnecessary diagnostic procedures. Our aim was to verify the suitability of five main NUS. Methods This single-center, retrospective, observational study analyzed a total number of 6474 valid cytologies. A full clinical and US description of the thyroid gland and nodules was performed. We retrospectively applied five available NUS: KTIRADS, ATA, AACE/ACE-AME, EUTIRADS, and ACRTIRADS. Thereafter, we calculated the sensitivity, specificity, PPV, and NPV, along with the number of possible fine-needle aspiration (FNA) sparing, according to each NUS algorithm and to clustering risk classes within three macro-groups (low, intermediate, and high risk). Results In a real-life setting of thyroid nodule management, available NUS scoring systems show good accuracy at ROC analysis (AUC up to 0.647) and higher NPV (up to 96%). The ability in FNA sparing ranges from 10 to 38% and reaches 44.2% of potential FNA economization in the low-risk macro-group. Considering our cohort, ACRTIRADS and AACE/ACE-AME scores provide the best compromise in terms of accuracy and spared cytology. Conclusions Despite several limitations, available NUS do appear to assist physicians in clinical practice. In the context of a common disease, such as thyroid nodules, higher accuracy and NPV are desirable NUS features. Further improvements in NUS sensitivity and specificity are attainable future goals to optimize nodule management. Key Points • Thyroid nodule ultrasound scores do assist clinicians in real practice. • Ultrasound scores reduce unnecessary diagnostic procedures, containing indolent thyroid microcarcinoma overdiagnosis. • The variable malignancy risk of the “indeterminate” category negatively influences score’s performance in real-life management of thyroid lesions.

Internal quality evaluation of chestnut using nuclear magnetic resonance

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Soo Hyun Park ◽  
Sang Ha Noh ◽  
Michael J. McCarthy ◽  
Seong Min Kim
Keyword(s):  
Image Processing ◽  
Nuclear Magnetic Resonance ◽  
Magnetic Resonance ◽  
Processing Algorithm ◽  
Internal Quality ◽  
Soluble Solid Content ◽  
Mr Images ◽  
Image Processing Algorithm ◽  
Internal Defects ◽  
Nuclear Magnetic

AbstractThis study was carried out to develop a prediction model for soluble solid content (SSC) of intact chestnut and to detect internal defects using nuclear magnetic resonance (NMR) relaxometry and magnetic resonance imaging (MRI). Inversion recovery and Carr–Purcell–Meiboom–Gill (CPMG) pulse sequences used to determine the longitudinal (T1) and transverse (T2) relaxation times, respectively. Partial least squares regression (PLSR) was adopted to predict SSCs of chestnuts with NMR data and histograms from MR images. The coefficient of determination (R2), root mean square error of prediction (RMSEP), ratio of prediction to deviation (RPD), and the ratio of error range (RER) of the optimized model to predict SSC were 0.77, 1.41 °Brix, 1.86, and 11.31 with a validation set. Furthermore, an image-processing algorithm has been developed to detect internal defects such as decay, mold, and cavity using MR images. The classification applied with the developed image processing algorithm was over 94% accurate to classify. Based on the results obtained, it was determined that the NMR signal could be applied for grading several levels by SSC, and MRI could be used to evaluate the internal qualities of chestnuts.

scholarly journals Can sonographic features of microcalcification predict thyroid nodule malignancy? a prospective observational study

2021 ◽  
Vol 52 (1) ◽  
Author(s):  
Mehrdad Nabahati ◽  
Rahele Mehraeen ◽  
Zoleika Moazezi ◽  
Naser Ghaemian
Keyword(s):  
Thyroid Nodule ◽  
Roc Analysis ◽  
Thyroid Nodules ◽  
Area Under The Curve ◽  
Positive Association ◽  
Needle Aspiration ◽  
Northern Iran ◽  
Significant Positive Association ◽  
Irregular Margin ◽  
Benign Nodules

Abstract Background The aim of this study was to investigate the diagnostic accuracy of microcalcification, as well as its associated sonographic features, for prediction of thyroid nodule malignancy. We prospectively assessed the patients with thyroid nodule, who underwent ultrasound-guided fine-needle aspiration during 2017–2020 in Babol, northern Iran. The ultrasonographic characteristics of the nodules, as well as their cytological results, were recorded. We used regression analysis to evaluate the relation between sonographic findings and nodule malignancy. A receiver operator characteristics (ROC) analysis was also used to estimate the ability of ultrasound to predict the characteristic features of malignancy, as estimated by the area under the curve (AUC). Results Overall, 1129 thyroid nodules were finally included in the study, of which 452 (40%) had microcalcification. A significant positive association was found between nodule malignancy and microcalcification in both univariate (OR=3.626, 95% CI 2.258–5.822) and multivariable regression analyses (OR=1.878, 95% CI 1.095–3.219). In the nodules with microcalcification, significant positive relations were seen between malignancy and hypoechogenicity (OR=3.833, 95% CI 1.032–14.238), >5 microcalcification number (OR=3.045, 95% CI 1.328–6.982), irregular margin (OR=3.341, 95% CI 1.078–10.352), and lobulated margin (OR=5.727, 95% CI 1.934–16.959). The ROC analysis indicated that AUC for hypoechogenicity, >5 microcalcification number, irregular margin, and lobulated margin were 60%, 62%, 55%, and 60%, respectively, in predicting malignant thyroid nodules. Conclusion The findings indicated that microcalcification can be a potential predictor of thyroid nodule malignancy. Also, the presence of irregular or lobulated margins, multiple intranodular microcalcification (>5 microcalcifications), and/or hypoechogenicity can improve the ability of microcalcification in distinguishing malignant from benign nodules.

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