Computer-aided Diagnosis: How to Move from the Laboratory to the Clinic

被引:214
作者
van Ginneken, Bram [1 ,2 ]
Schaefer-Prokop, Cornelia M. [1 ,4 ,5 ]
Prokop, Mathias [1 ,3 ]
机构
[1] Radboud Univ Nijmegen, Med Ctr, Dept Radiol, NL-6525 ED Nijmegen, Netherlands
[2] Univ Med Ctr, Image Sci Inst, NL-3584 CX Utrecht, Netherlands
[3] Univ Med Ctr, Dept Radiol, NL-3584 CX Utrecht, Netherlands
[4] Meander Med Ctr, Dept Radiol, Amersfoort, Netherlands
[5] Univ Amsterdam, Acad Med Ctr, Dept Radiol, NL-1105 AZ Amsterdam, Netherlands
关键词
LUNG-CANCER; PULMONARY NODULES; TOMOGRAPHY SCANS; RISK-ASSESSMENT; BONE-AGE; CT; EMPHYSEMA; CAD; CLASSIFICATION; SEGMENTATION;
D O I
10.1148/radiol.11091710
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
Computer-aided diagnosis (CAD), encompassing computer-aided detection and quantification, is an established and rapidly growing field of research. In daily practice, however, most: radiologists do not yet use CAD routinely. This article discusses how to move CAD from the laboratory to the clinic. The authors review the principles of CAD for lesion detection and for quantification and illustrate the state-of-the-art with various examples. The requirements that radiologists have for CAD are discussed: sufficient performance, no increase in reading time, seamless work-flow integration, regulatory approval, and cost efficiency. Performance is still the major bottleneck for many CAD systems. Novel ways of rising CAD, extending the traditional paradigm of displaying markers for a second look, may be the key to using the technology effectively. The most promising strategy to improve CAD is the creation of publicly, available databases for training and validation. This can identify the most: fruitful new research directions, and provide a platform to combine multiple approaches for a single task to create superior algorithms. (C) RSNA, 2011
引用
收藏
页码:719 / 732
页数:14
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