提高循环肿瘤细胞（CTC）检测的准确性：计算机辅助检测
Improving the accuracy of CTC interpretation: computer-aided detection.
作者信息
Summers Ronald M
机构信息
Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, National Institutes of Health Clinical Center, Building 10, Room 1C368X MSC 1182, Bethesda, MD 20892-1182, USA.
出版信息
Gastrointest Endosc Clin N Am. 2010 Apr;20(2):245-57. doi: 10.1016/j.giec.2010.02.004.
Abstract
Computer-aided polyp detection aims to improve the accuracy of the colonography interpretation. The computer searches the colonic wall to look for polyplike protrusions and presents a list of suspicious areas to a physician for further analysis. Computer-aided polyp detection has developed rapidly in the past decade in the laboratory setting and has sensitivities comparable with those of experts. Computer-aided polyp detection tends to help inexperienced readers more than experienced ones and may also lead to small reductions in specificity. In its currently proposed use as an adjunct to standard image interpretation, computer-aided polyp detection serves as a spellchecker rather than an efficiency enhancer.
摘要
计算机辅助息肉检测旨在提高结肠造影解读的准确性。计算机搜索结肠壁以寻找息肉样突起，并将可疑区域列表呈现给医生进行进一步分析。在过去十年中，计算机辅助息肉检测在实验室环境中发展迅速，其灵敏度与专家相当。计算机辅助息肉检测对经验不足的阅片者的帮助往往大于经验丰富的阅片者，也可能导致特异性略有降低。在目前提议作为标准图像解读辅助手段的应用中，计算机辅助息肉检测起到的是拼写检查器的作用，而非效率增强器。
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1
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AJR Am J Roentgenol. 2009 Nov;193(5):1305-10. doi: 10.2214/AJR.09.2442.
2
Nonlinear regression-based method for pseudoenhancement correction in CT colonography.
Med Phys. 2009 Aug;36(8):3596-606. doi: 10.1118/1.3147201.
3
Computer-aided detection of polyps in CT colonography using logistic regression.
IEEE Trans Med Imaging. 2010 Jan;29(1):120-31. doi: 10.1109/TMI.2009.2028576. Epub 2009 Aug 7.
4
An anthropomorphic phantom study of computer-aided detection performance for polyp detection on CT colonography: a comparison of commercially and academically available systems.
AJR Am J Roentgenol. 2009 Aug;193(2):445-54. doi: 10.2214/AJR.08.1555.
5
Virtual tagging for laxative-free CT colonography: pilot evaluation.
Med Phys. 2009 May;36(5):1830-8. doi: 10.1118/1.3113893.
6
Impact of computer-aided detection on the cost-effectiveness of CT colonography.
Radiology. 2009 Feb;250(2):488-97. doi: 10.1148/radiol.2502080685.
7
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Radiology. 2009 Feb;250(2):354-62. doi: 10.1148/radiol.2502080224.
8
An EM approach to MAP solution of segmenting tissue mixture percentages with application to CT-based virtual colonoscopy.
Med Phys. 2008 Dec;35(12):5787-98. doi: 10.1118/1.3013591.
9
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10
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