Size assessment of breast lesions by means of a computer-aided detection (CAD) system for magnetic resonance mammography

2011 ◽  
Vol 116 (7) ◽  
pp. 1039-1049 ◽  
Author(s):  
G. Levrini ◽  
R. Sghedoni ◽  
C. Mori ◽  
A. Botti ◽  
R. Vacondio ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Breast Lesions ◽  
Computer Aided Detection ◽  
Cad System ◽  
Computer Aided ◽  
Magnetic Resonance Mammography

scholarly journals Sensitivity of Computer-Aided Detection (CAD) Device for Lung Nodule Detection on Chest Radiography: A Real-Life Application

2020 ◽  
pp. 1-5
Author(s):  
Ammar Chaudhry ◽  
Ammar Chaudhry ◽  
William H. Moore
Keyword(s):  
Sensitivity And Specificity ◽  
Chest Radiography ◽  
Predictive Value ◽  
Lung Nodule ◽  
Chest Radiographs ◽  
Lung Nodules ◽  
Computer Aided Detection ◽  
Cad System ◽  
Computer Aided ◽  
The Mean

Purpose: The radiographic diagnosis of lung nodules is associated with low sensitivity and specificity. Computer-aided detection (CAD) system has been shown to have higher accuracy in the detection of lung nodules. The purpose of this study is to assess the effect on sensitivity and specificity when a CAD system is used to review chest radiographs in real-time setting. Methods: Sixty-three patients, including 24 controls, who had chest radiographs and CT within three months were included in this study. Three radiologists were presented chest radiographs without CAD and were asked to mark all lung nodules. Then the radiologists were allowed to see the CAD region-of-interest (ROI) marks and were asked to agree or disagree with the marks. All marks were correlated with CT studies. Results: The mean sensitivity of the three radiologists without CAD was 16.1%, which showed a statistically significant improvement to 22.5% with CAD. The mean specificity of the three radiologists was 52.5% without CAD and decreased to 48.1% with CAD. There was no significant change in the positive predictive value or negative predictive value. Conclusion: The addition of a CAD system to chest radiography interpretation statistically improves the detection of lung nodules without affecting its specificity. Thus suggesting CAD would improve overall detection of lung nodules.

Abstract T P32: Computer Aided Detection of Asymptomatic Cerebral Microbleeds on Magnetic Resonance Images

Stroke ◽  
2014 ◽  
Vol 45 (suppl_1) ◽  
Author(s):  
Yan Li ◽  
Yining Huang ◽  
Jue Zhang ◽  
Jing Fang
Keyword(s):  
Magnetic Resonance ◽  
Rating Scale ◽  
Characteristic Curve ◽  
Magnetic Resonance Images ◽  
False Positives ◽  
Cerebral Microbleeds ◽  
Cad System ◽  
Automated Method ◽  
Skull Stripping ◽  
Computer Aided

Purpose: Manual rating of Cerebral microbleeds (CMBs) is time-consuming and inconsistent. Since the presence and number of CMBs have become a potential diagnostic and prognostic biomarker of stroke, an automatic identification method is required. We proposed a computer aided diagnosis (CAD) system for the detection of the CMBs on the magnetic resonance (MR) images automatically. Methods: Eighty-one patients were recruited in this study. CMBs on the MR T2* weighted images were manually rated according to the Microbleed Anatomic Rating Scale (MARS) criteria. Our automated method consisted of two steps: i) Pre-processing: After skull stripping, isolated islands of points were removed while holes were restored to avoid over segmentation. Local threshold segmentation was applied for the initial candidate selection. ii) Identification model: Seven features were extracted from each candidate: area, roundness, intensity, average of the boundary, contrast, shape-intensity and location-mark (according to the probability density templates calculated from the location information of the CMBs). For further identification of each candidate, Random Forest (RF) model was used to distinguish CMBs from the mimics. Results: A total of 337 CMBs in the 81 patients were studied. Comparing with the counting from the experienced doctors, high sensitivity of 92% (310/337) was achieved after pre-processing. The RF model eliminated most of the false-positives while maintaining a reliable sensitivity of 94% (291/310) and specificity of 96% (4272/4450). The area under the Receiver operating characteristic curve was 0.98 ± 0.02 for the detection model. In summary, this CAD system had an overall sensitivity of 86% (291/337) and specificity of 96% (4272/4450), producing only 2.2 false-positives per subject. Conclusion: This presented strategy is technically effective. The results indicate that it has the potential to be used for clinical detection of CMBs.

scholarly journals Computer-aided detection for prostate cancer diagnosis based on magnetic resonance imaging

Medicine ◽  
2019 ◽  
Vol 98 (29) ◽  
pp. e16326 ◽  
Author(s):  
Fuxiang Liang ◽  
Meixuan Li ◽  
Liang Yao ◽  
Xiaoqin Wang ◽  
Jieting Liu ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Prostate Cancer ◽  
Magnetic Resonance ◽  
Cancer Diagnosis ◽  
Computer Aided Detection ◽  
Resonance Imaging ◽  
Prostate Cancer Diagnosis ◽  
Computer Aided

scholarly journals Computer-Aided Detection of Metastatic Brain Tumors in Magnetic Resonance Images

2013 ◽  
Vol 69 (6) ◽  
pp. 632-640 ◽  
Author(s):  
Tomomi Takenaga ◽  
Yoshikazu Uchiyama ◽  
Toshinori Hirai ◽  
Hideo Nakamura ◽  
Yutaka Kai ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Brain Tumors ◽  
Magnetic Resonance Images ◽  
Computer Aided Detection ◽  
Metastatic Brain Tumors ◽  
Computer Aided

scholarly journals Computer Aided Detection System for Prediction of the Malaise during Hemodialysis

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Sabina Tangaro ◽  
Annarita Fanizzi ◽  
Nicola Amoroso ◽  
Roberto Corciulo ◽  
Elena Garuccio ◽  
...  
Keyword(s):  
Detection System ◽  
Clinical Status ◽  
Classification Performance ◽  
Stress Factors ◽  
Computer Aided Detection ◽  
Cad System ◽  
Monitoring Strategies ◽  
Computer Aided ◽  
Clinical Conditions

Monitoring of dialysis sessions is crucial as different stress factors can yield suffering or critical situations. Specialized personnel is usually required for the administration of this medical treatment; nevertheless, subjects whose clinical status can be considered stable require different monitoring strategies when compared with subjects with critical clinical conditions. In this case domiciliary treatment or monitoring can substantially improve the quality of life of patients undergoing dialysis. In this work, we present aComputer Aided Detection(CAD) system for the telemonitoring of patients’ clinical parameters. The CAD was mainly designed to predict the insurgence of critical events; it consisted of twoRandom Forest(RF) classifiers: the first one (RF1) predicting the onset of any malaise one hour after the treatment start and the second one (RF2) again two hours later. The developed system shows an accurate classification performance in terms of bothsensitivityandspecificity. Thespecificityin the identification of nonsymptomatic sessions and thesensitivityin the identification of symptomatic sessions forRF2are equal to 86.60% and 71.40%, respectively, thus suggesting the CAD as an effective tool to support expert nephrologists in telemonitoring the patients.

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