scholarly journals Validation of radiologists’ findings by computer-aided detection (CAD) software in breast cancer detection with automated 3D breast ultrasound: a concept study in implementation of artificial intelligence software

2019 ◽  
Vol 61 (3) ◽  
pp. 312-320 ◽  
Author(s):  
Jan CM van Zelst ◽  
Tao Tan ◽  
Ritse M Mann ◽  
Nico Karssemeijer
Keyword(s):  
Breast Cancer ◽  
Area Under The Curve ◽  
Breast Ultrasound ◽  
Computer Aided Detection ◽  
Benign Lesions ◽  
Partial Area ◽  
Computer Aided ◽  
Before And After ◽  
Detection Software ◽  
Automated Breast Ultrasound

Background Computer-aided detection software for automated breast ultrasound has been shown to have potential in improving the accuracy of radiologists. Alternative ways of implementing computer-aided detection, such as independent validation or preselecting suspicious cases, might also improve radiologists’ accuracy. Purpose To investigate the effect of using computer-aided detection software to improve the performance of radiologists by validating findings reported by radiologists during screening with automated breast ultrasound. Material and Methods Unilateral automated breast ultrasound exams were performed in 120 women with dense breasts that included 60 randomly selected normal exams, 30 exams with benign lesions, and 30 malignant cases (20 mammography-negative). Eight radiologists were instructed to detect breast cancer and rate lesions using BI-RADS and level-of-suspiciousness scores. Computer-aided detection software was used to check the validity of radiologists' findings. Findings found negative by computer-aided detection were not included in the readers’ performance analysis; however, the nature of these findings were further analyzed. The area under the curve and the partial area under the curve for an interval in the range of 80%–100% specificity before and after validation of computer-aided detection were compared. Sensitivity was computed for all readers at a simulation of 90% specificity. Results Partial AUC improved significantly from 0.126 (95% confidence interval [CI] = 0.098–0.153) to 0.142 (95% CI = 0.115–0.169) ( P = 0.037) after computer-aided detection rejected mostly benign lesions and normal tissue scored BI-RADS 3 or 4. The full areas under the curve (0.823 vs. 0.833, respectively) were not significantly different ( P = 0.743). Four cancers detected by readers were completely missed by computer-aided detection and four other cancers were detected by both readers and computer-aided detection but falsely rejected due to technical limitations of our implementation of computer-aided detection validation. In this study, validation of computer-aided detection discarded 42.6% of findings that were scored BI-RADS ≥3 by the radiologists, of which 85.5% were non-malignant findings. Conclusion Validation of radiologists’ findings using computer-aided detection software for automated breast ultrasound has the potential to improve the performance of radiologists. Validation of computer-aided detection might be an efficient tool for double-reading strategies by limiting the amount of discordant cases needed to be double-read.

Rapid image stitching and computer-aided detection for multipass automated breast ultrasound

2010 ◽  
Vol 37 (5) ◽  
pp. 2063-2073 ◽  
Author(s):  
Ruey-Feng Chang ◽  
Kuang-Che Chang-Chien ◽  
Etsuo Takada ◽  
Chiun-Sheng Huang ◽  
Yi-Hong Chou ◽  
...  
Keyword(s):  
Breast Ultrasound ◽  
Image Stitching ◽  
Computer Aided Detection ◽  
Computer Aided ◽  
Automated Breast Ultrasound

Improved cancer detection in automated breast ultrasound by radiologists using Computer Aided Detection

2017 ◽  
Vol 89 ◽  
pp. 54-59 ◽  
Author(s):  
J.C.M. van Zelst ◽  
T. Tan ◽  
B. Platel ◽  
M. de Jong ◽  
A. Steenbakkers ◽  
...  
Keyword(s):  
Cancer Detection ◽  
Breast Ultrasound ◽  
Computer Aided Detection ◽  
Computer Aided ◽  
Automated Breast Ultrasound

scholarly journals Improved computer-aided detection of pulmonary nodules via deep learning in the sinogram domain

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Yongfeng Gao ◽  
Jiaxing Tan ◽  
Zhengrong Liang ◽  
Lihong Li ◽  
Yumei Huo
Keyword(s):  
Deep Learning ◽  
Area Under The Curve ◽  
Pulmonary Nodules ◽  
Ct Image ◽  
Computer Aided Detection ◽  
Ct Scanner ◽  
Human Visual Perception ◽  
Computer Aided ◽  
Detection And Diagnosis ◽  
Cade System

AbstractComputer aided detection (CADe) of pulmonary nodules plays an important role in assisting radiologists’ diagnosis and alleviating interpretation burden for lung cancer. Current CADe systems, aiming at simulating radiologists’ examination procedure, are built upon computer tomography (CT) images with feature extraction for detection and diagnosis. Human visual perception in CT image is reconstructed from sinogram, which is the original raw data acquired from CT scanner. In this work, different from the conventional image based CADe system, we propose a novel sinogram based CADe system in which the full projection information is used to explore additional effective features of nodules in the sinogram domain. Facing the challenges of limited research in this concept and unknown effective features in the sinogram domain, we design a new CADe system that utilizes the self-learning power of the convolutional neural network to learn and extract effective features from sinogram. The proposed system was validated on 208 patient cases from the publicly available online Lung Image Database Consortium database, with each case having at least one juxtapleural nodule annotation. Experimental results demonstrated that our proposed method obtained a value of 0.91 of the area under the curve (AUC) of receiver operating characteristic based on sinogram alone, comparing to 0.89 based on CT image alone. Moreover, a combination of sinogram and CT image could further improve the value of AUC to 0.92. This study indicates that pulmonary nodule detection in the sinogram domain is feasible with deep learning.

scholarly journals Automated Breast Ultrasound: Technical Aspects, Impact on Breast Screening, and Future Perspectives

2021 ◽  
Author(s):  
Iris Allajbeu ◽  
Sarah E Hickman ◽  
Nicholas Payne ◽  
Penelope Moyle ◽  
Kathryn Taylor ◽  
...  
Keyword(s):  
Cancer Detection ◽  
Breast Screening ◽  
Screening Tool ◽  
Imaging Technique ◽  
Breast Ultrasound ◽  
Computer Aided Detection ◽  
Future Perspectives ◽  
Technical Aspects ◽  
Dense Breasts ◽  
Automated Breast Ultrasound

Abstract Purpose of Review Automated breast ultrasound (ABUS) is a three-dimensional imaging technique, used as a supplemental screening tool in women with dense breasts. This review considers the technical aspects, pitfalls, and the use of ABUS in screening and clinical practice, together with new developments and future perspectives. Recent Findings ABUS has been approved in the USA and Europe as a screening tool for asymptomatic women with dense breasts in addition to mammography. Supplemental US screening has high sensitivity for cancer detection, especially early-stage invasive cancers, and reduces the frequency of interval cancers. ABUS has similar diagnostic performance to handheld ultrasound (HHUS) and is designed to overcome the drawbacks of operator dependence and poor reproducibility. Concerns with ABUS, like HHUS, include relatively high recall rates and lengthy reading time when compared to mammography. ABUS is a new technique with unique features; therefore, adequate training is required to improve detection and reduce false positives. Computer-aided detection may reduce reading times and improve cancer detection. Other potential applications of ABUS include local staging, treatment response evaluation, breast density assessment, and integration of radiomics. Summary ABUS provides an efficient, reproducible, and comprehensive supplemental imaging technique in breast screening. Developments with computer-aided detection may improve the sensitivity and specificity as well as radiologist confidence and reduce reading times, making this modality acceptable in large volume screening centers.

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