A CAD Utilizing 3D Massive-Training ANNs for Detection of Flat Lesions in CT Colonography: Preliminary Results
نویسندگان
چکیده
Our purpose was to develop a computer-aided diagnostic (CAD) scheme for detection of flat lesions (also known as superficial elevated or depressed lesions) in CT colonography (CTC), which utilized 3D massive-training artificial neural networks (MTANNs) for false-positive (FP) reduction. Our CAD scheme consisted of colon segmentation, polyp candidate detection, linear discriminant analysis, and MTANNs. To detect flat lesions, we developed a precise shape analysis in the polyp detection step to accommodate the analysis to include a flat shape. With our MTANN CAD scheme, 68% (19/28) of flat lesions, including six lesions “missed” by radiologists in a multicenter clinical trial, were detected correctly, with 10 (249/25) FPs per patient.
منابع مشابه
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Computer-aided diagnosis (CAD) (Giger and Suzuki 2007) has been an active area of study in medical image analysis, because evidence suggests that CAD can help improve the diagnostic performance of radiologists in their image interpretations (Li, Aoyama et al. 2004; Li, Arimura et al. 2005; Dean and Ilvento 2006). Many investigators have participated in and developed CAD schemes for detection/di...
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One of the major challenges in computer-aided detection (CAD) of polyps in CT colonography (CTC) is the reduction of false-positive detections (FPs) without a concomitant reduction in sensitivity. A large number of FPs is likely to confound the radiologist's task of image interpretation, lower the radiologist's efficiency, and cause radiologists to lose their confidence in CAD as a useful tool....
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