Speckle reduction imaging of breast ultrasound does not improve the diagnostic performance of morphology-based CAD System

2011 ◽  
Vol 40 (1) ◽  
pp. 1-6 ◽  
Author(s):  
Hsin-Shun Tseng ◽  
Hwa-Koon Wu ◽  
Shou-Tung Chen ◽  
Shou-Jen Kuo ◽  
Yu-Len Huang ◽  
...  
Keyword(s):  
Diagnostic Performance ◽  
Breast Ultrasound ◽  
Speckle Reduction ◽  
Cad System ◽  
Speckle Reduction Imaging

1467: Speckle Reduction Imaging in Breast Ultrasound does not Improve Diagnostic Performance

2009 ◽  
Vol 35 (8) ◽  
pp. S226
Author(s):  
Yu-Fen Wang ◽  
Shu-Fen Chiang ◽  
Yu-Len Huang ◽  
Dar-Ren Chen ◽  
Shou-Jen Kuo ◽  
...  
Keyword(s):  
Diagnostic Performance ◽  
Breast Ultrasound ◽  
Speckle Reduction ◽  
Speckle Reduction Imaging

scholarly journals Evaluation of the effect of computer aided diagnosis system on breast ultrasound for inexperienced radiologists in describing and determining breast lesions

2019 ◽  
Vol 21 (3) ◽  
pp. 239
Author(s):  
Jeongmin Lee ◽  
Sanghee Kim ◽  
Bong Joo Kang ◽  
Sung Hun Kim ◽  
Ga Eun Park
Keyword(s):  
Breast Imaging ◽  
Diagnostic Performance ◽  
Computer Aided Diagnosis ◽  
Breast Ultrasound ◽  
Breast Lesions ◽  
Cad System ◽  
Computer Aided ◽  
Kappa Value ◽  
Inexperienced Reader ◽  
Aided Diagnosis

Aim: To investigate the effect of a computer-aided diagnosis (CAD) system on breast ultrasound (US) for inexperienced radiologists in describing and determining breast lesions.Materials and methods: Between October 2015 to January 2017, 500 suspicious or probable benign lesions in 413 patients were reviewed. Five experienced readers retrospectively reviewed for each of 100 lesions according to the Breast Imaging Reporting and Data System (BI-RADS) lexicon and category, with CAD system (S-detectTM). The readers then made final decisions by combining CAD results to their US results. Using the nested experiment design, five inexperienced readers were asked to select the appropriate BI-RADS lexicons, categories, CAD results, and combination results for each of the 100 lesions, retrospectively. Diagnostic performance of experienced and inexperienced radiologists and CAD were assessed. For each case, agreements in the lexicons and categories were analyzed among the experienced reader, inexperienced reader and CAD.Results: Indicators of the diagnostic performance for breast malignancy of the experienced group (AUC=0.83, 95%CI [0.80, 0.86]) were similar or higher than those of CAD (AUC = 0.79, 95%CI[0.74, 0.83], p=0.101), except for specificity. Conversely, indicators of diagnostic performance of inexperienced group (AUC=0.65, 95%CI[0.58, 0.71]) did not differ from or were lower than those of CAD(AUC=0.73, 95%CI[0.67, 0.78], p=0.013). Also, the diagnostic performance of the inexperienced group after combination with the CAD result was significantly improved (0.71, 95% CI [0.65, 0.77], p=0.001), whereas that of the experienced group did not change after combination with the CAD result, except for specificity and positive predictive value (PPV). Kappa values for the agreement of the categorization between CAD and each radiologist group were increased after applying the CAD result to their result of general US. Especially, the increase of the Kappa value was higher in the inexperienced group than in the experienced group. Also, for all the lexicons, the Kappa values between the experienced group and CAD were higher than those between the inexperienced group and CAD.Conclusion: By using the CAD system for classification of breast lesions, diagnostic performance of the inexperienced radiologists for malignancy was significantly improved, and better agreement was observed in lexicons between the experienced group and CAD than between the inexperienced group and CAD. CAD may be beneficial and educational for the inexperienced group.

scholarly journals Additional Breast Ultrasound Examinations in Clustered Calcifications: for Improving Diagnostic Performance

2009 ◽  
Vol 12 (3) ◽  
pp. 142 ◽  
Author(s):  
Hee Young Kim ◽  
Bo Kyoung Seo ◽  
Hee-Young Kim ◽  
Ann Yie ◽  
Kyu Ran Cho ◽  
...  
Keyword(s):  
Diagnostic Performance ◽  
Breast Ultrasound

scholarly journals A Novel Computer-Aided-Diagnosis System for Breast Ultrasound Images Based on BI-RADS Categories

2020 ◽  
Vol 10 (5) ◽  
pp. 1830
Author(s):  
Yi-Wei Chang ◽  
Yun-Ru Chen ◽  
Chien-Chuan Ko ◽  
Wei-Yang Lin ◽  
Keng-Pei Lin
Keyword(s):  
Breast Tumor ◽  
Breast Imaging ◽  
Breast Lesion ◽  
Low Cost ◽  
Breast Ultrasound ◽  
Tissue Imaging ◽  
Support Vector ◽  
Ultrasound Images ◽  
Traditional Assessment ◽  
Cad System

The breast ultrasound is not only one of major devices for breast tissue imaging, but also one of important methods in breast tumor screening. It is non-radiative, non-invasive, harmless, simple, and low cost screening. The American College of Radiology (ACR) proposed the Breast Imaging Reporting and Data System (BI-RADS) to evaluate far more breast lesion severities compared to traditional diagnoses according to five-criterion categories of masses composition described as follows: shape, orientation, margin, echo pattern, and posterior features. However, there exist some problems, such as intensity differences and different resolutions in image acquisition among different types of ultrasound imaging modalities so that clinicians cannot always identify accurately the BI-RADS categories or disease severities. To this end, this article adopted three different brands of ultrasound scanners to fetch breast images for our experimental samples. The breast lesion was detected on the original image using preprocessing, image segmentation, etc. The breast tumor’s severity was evaluated on the features of the breast lesion via our proposed classifiers according to the BI-RADS standard rather than traditional assessment on the severity; i.e., merely using benign or malignant. In this work, we mainly focused on the BI-RADS categories 2–5 after the stage of segmentation as a result of the clinical practice. Moreover, several features related to lesion severities based on the selected BI-RADS categories were introduced into three machine learning classifiers, including a Support Vector Machine (SVM), Random Forest (RF), and Convolution Neural Network (CNN) combined with feature selection to develop a multi-class assessment of breast tumor severity based on BI-RADS. Experimental results show that the proposed CAD system based on BI-RADS can obtain the identification accuracies with SVM, RF, and CNN reaching 80.00%, 77.78%, and 85.42%, respectively. We also validated the performance and adaptability of the classification using different ultrasound scanners. Results also indicate that the evaluations of F-score based on CNN can obtain measures higher than 75% (i.e., prominent adaptability) when samples were tested on various BI-RADS categories.

Diagnostic performance of a CAD system in the detection of colonic polyps in virtual colonoscopy

2005 ◽  
Vol 43 (05) ◽  
Author(s):  
Zs Tarján ◽  
J Koloszár ◽  
B Forgács ◽  
J Wacha ◽  
G Kovács ◽  
...  
Keyword(s):  
Diagnostic Performance ◽  
Virtual Colonoscopy ◽  
Colonic Polyps ◽  
Cad System

Breast Ultrasound Image Processing

2021 ◽  
pp. 44-69
Author(s):  
Strivathsav Ashwin Ramamoorthy ◽  
Varun P. Gopi
Keyword(s):  
Ultrasound Image ◽  
Breast Ultrasound ◽  
Ultrasound Images ◽  
Low Contrast ◽  
Cad Systems ◽  
Cad System ◽  
Ultrasound Image Processing ◽  
Serious Disease ◽  
Aided Diagnosis ◽  
Enhancement Algorithm

Breast cancer is a serious disease among women, and its early detection is very crucial for the treatment of cancer. To assist radiologists who manually delineate the tumour from the ultrasound image an automatic computerized method of detection called CAD (computer-aided diagnosis) is developed to provide valuable inputs for radiologists. The CAD systems is divided into many branches like pre-processing, segmentation, feature extraction, and classification. This chapter solely focuses on the first two branches of the CAD system the pre-processing and segmentation. Ultrasound images acquired depends on the operator expertise and is found to be of low contrast and fuzzy in nature. For the pre-processing branch, a contrast enhancement algorithm based on fuzzy logic is implemented which could help in the efficient delineation of the tumour from ultrasound image.

scholarly journals Breast ultrasound diagnostic performance and outcomes for mass lesions using Breast Imaging Reporting and Data System category 0 mammogram

Clinics ◽  
2011 ◽  
Vol 66 (3) ◽  
pp. 443-448 ◽  
Author(s):  
Paulo Almazy Zanello ◽  
Andre Felipe Cica Robim ◽  
Tatiane Mendes Gonçalves de Oliveira ◽  
Jorge Elias Junior ◽  
Jurandyr Moreira de Andrade ◽  
...  
Keyword(s):  
Breast Imaging ◽  
Diagnostic Performance ◽  
Breast Ultrasound ◽  
Data System ◽  
Mass Lesions

Diagnostic performance of automated breast ultrasound and handheld ultrasound in women with dense breasts

2020 ◽  
Vol 181 (3) ◽  
pp. 589-597 ◽  
Author(s):  
Mengmeng Jia ◽  
Xi Lin ◽  
Xiang Zhou ◽  
Huijiao Yan ◽  
Yaqing Chen ◽  
...  
Keyword(s):  
Diagnostic Performance ◽  
Breast Ultrasound ◽  
Dense Breasts ◽  
Handheld Ultrasound ◽  
Automated Breast Ultrasound
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