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24 - CAD: An Image Perception Perspective

from Part V - Computational Perception

Published online by Cambridge University Press:  20 December 2018

By
MaryEllen Giger  and
Weijie Chen
Edited by
Ehsan Samei  and
Elizabeth A. Krupinski
Ehsan Samei
Affiliation:
Duke University Medical Center, Durham
Elizabeth A. Krupinski
Affiliation:
Emory University, Atlanta
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The Handbook of Medical Image Perception and Techniques , pp. 359 - 373
Publisher: Cambridge University Press
Print publication year: 2018

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References

American College of Radiology. (2003). ACR BI-RADS: MRI. In: ACR BI-RADS: Breast Imaging Reporting and Data System: Breast Imaging Atlas. Reston, VA: ACR.Google Scholar
Barrett, H.H., Kupinski, M.A., Clarkson, E. (2005). Probabilistic foundations of the MRMC method. Proc SPIE, 5749, 21–31.CrossRefGoogle Scholar
Beiden, S.V., Wagner, R.F., Campbell, G. (2000). Components-of-variance models and multiple-bootstrap experiments: an alternative method for random-effects, receiver operating characteristic analysis. Acad Radiol, 7, 341–349.Google Scholar
Bird, R.E., Wallace, T.W., Yankaskas, B.C. (1992). Analysis of cancers missed at screening mammography. Radiology, 184, 613–617.CrossRefGoogle ScholarPubMed
Birdwell, R.L., Bandodkar, P., Ikeda, D.M. (2005). Computer-aided detection with screening mammography in a university hospital setting. Radiology, 236, 451–457.Google Scholar
Bornefalk, H., Hermansson, A.B. (2005). On the comparison of FROC curves in mammography CAD systems. Med Phys, 32, 412–417.Google Scholar
Bunch, P.C., Hamilton, J.F., Sanderson, G.K., Simmons, A.H. (1977). A free response approach to the measurement and characterization of radiographic observer performance. Proc SPIE, 127, 124–135.Google Scholar
Chakraborty, D.P. (2000). The FROC, AFROC and DROC variants of the ROC analysis. In: Beutel, J., Kundel, H., Van Metter, R. (eds.) Handbook of Medical Imaging, Volume 1. Physics and Psychophysics. Bellingham, WA: SPIE, pp. 771–798.CrossRefGoogle Scholar
Chan, H.P., Sahiner, B., Helvie, M.A., et al. (1999). Improvement of radiologists’ characterization of mammographic masses by using computer-aided diagnosis: an ROC study. Radiology, 212, 817–827.Google Scholar
Chen, W., Giger, M.L., Bick, U. (2006a). A fuzzy c-means (FCM) based approach for computerized segmentation of breast lesions in dynamic contrast-enhanced MR images. Acad Radiol, 16, 63–72.Google Scholar
Chen, W., Giger, M.L., Bick, U., Newstead, G. (2006b). Automatic identification and classification of characteristic kinetic curves of breast lesions on DCE-MRI. Med Phys, 33, 2878–2887.CrossRefGoogle ScholarPubMed
Clarkson, E., Kupinski, M.A., Barrett, H.H. (2006). A probabilistic model for the MRMC method. Part I. Theoretical development. Acad Radiol, 13, 1410–1421.Google Scholar
Cupples, T., Cunningham, J.E., Reynolds, J.C. (2005). Impact of computer-aided detection in a regional screening mammography program. AJR, 185, 944–950.Google Scholar
Dean, J.C., Iivento, C.C. (2006). Improved cancer detection using computer-aided detection with diagnostic and screening mammography: prospective study of 104 cancers. AJR, 187, 20–28.Google Scholar
Dorfman, D.D., Berbaum, K.S., Metz, C.E. (1992). Receiver operating characteristic rating analysis: generalization to the population of readers and patients with the jackknife method. Invest Radiol, 27, 723–731.Google Scholar
Feig, S.A., Sickes, E.A., Evans, W.P., Linver, M.N. (2004). Re: Changes in breast cancer detection and mammography recall rates after the introduction of a computer-aided detection system. J Natl Cancer Inst, 96, 1260–1261.Google Scholar
Fenton, J.J., Taplin, S.H., Carney, P.A., et al. (2007). Influence of computer-aided detection on performance of screening mammography. N Engl J Med, 356, 1399–1409.Google Scholar
Flehinger, B.J., Kimmel, M., Melamed, M.R. (1992). The effect of surgical treatment on survival from early lung cancer. Implications for screening. Chest, 101, 1013–1018.Google Scholar
Freedman, M., Lo, S., Lure, F., et al. (2001). Computer-aided detection of lung cancer on chest radiographs: algorithm performance vs. radiologists’ performance by size of cancer. Proc SPIE, 4319, 150–159.Google Scholar
Freer, T.W., Ulissey, M.J. (2001). Screening mammography with computer-aided detection. prospective study of 12,860 patients in a community breast center. Radiology, 222, 781–786.Google Scholar
Gallas, B.D. (2006). One-shot estimate of MRMC variance: AUC. Acad Radiol, 13, 353–362.CrossRefGoogle ScholarPubMed
Gallas, B.D., Brown, D.G. (2008). Reader studies for validation of CAD systems. Neural Networks, 21, 387–397.CrossRefGoogle ScholarPubMed
Gallas, B.D., Pennello, G.A., Myers, K.J. (2007). Multi-reader multi-case variance analysis for binary data. J Opt Soc Am A, 24(12), B70–B80.Google Scholar
Giger, M.L., Huo, Z., Kupinski, M.A., Vyborny, C.J. (2000). Computer-aided diagnosis in mammography. In: Sonka, M., Fitzpatrick, M.J. (eds.) Handbook of Medical Imaging, Volume 2. Medical Imaging Processing and Analysis. Bellingham, WA: SPIE, pp. 915–1004.Google Scholar
Giger, M.L., Huo, Z., Vyborny, C.J., et al. (2003). Results of an observer study with an intelligent mammographic workstation for CAD. In: Peitgen, H.-O. (ed.) Digital Mammography, IWDM 2002. Berlin: Springer, pp. 297–303.Google Scholar
Gromet, M. (2008). Comparison of computer-aided detection to double reading of screening mammograms: review of 231,221 mammograms. Am J Roentgenol, 190(4), 854–859.Google Scholar
Gur, D., Sumkin, J.H., Rockette, H.E., et al. (2004). Changes in breast cancer detection and mammography recall rates after the introduction of a computer-aided detection system. J Natl Cancer Inst, 96, 185–190.Google Scholar
Gur, D., Bandos, A.I., Cohen, C.S., et al. (2008). The “laboratory” effect: comparing radiologists’ performance and variability during prospective clinical and laboratory mammography interpretations. Radiology, 249, 47–53.Google Scholar
Hara, A.K., Johnson, C.D., Reed, J.E., et al. (1997). Detection of colorectal polyps with CT colography: initial assessment of sensitivity and specificity. Radiology, 205, 59–65.Google Scholar
Helvie, M., Hadjiiski, L., Makariou, E., et al. (2004). Sensitivity of noncommercial computer-aided detection system for mammographic breast cancer detection: pilot clinical trial. Radiology, 231, 208–214.Google Scholar
Henschke, C.I., Naidich, D.P., Yankelevitz, D.F., et al. (2001). Early lung cancer action project: initial findings on repeat screenings. Cancer, 92, 153–159.3.0.CO;2-S>CrossRefGoogle ScholarPubMed
Horsch, K., Giger, M.L., Vyborny, C.J., et al. (2006). Multi-modality computer-aided diagnosis for the classification of breast lesions: observer study results on an independent clinical dataset. Radiology, 240, 357–368.Google Scholar
Huo, Z., Giger, M.L., Vyborny, C.J., et al. (1995). Analysis of spiculation in the computerized classification of mammographic masses. Med Phys, 22, 1569–1579.Google Scholar
Huo, Z., Giger, M.L., Vyborny, C.J., et al. (2002). Effectiveness of CAD in the diagnosis of breast cancer: an observer study on an independent database of mammograms. Radiology, 224, 560–568.Google Scholar
Jiang, Y., Metz, C.E., Nishikawa, R.M. (1996). A receiver operating characteristic partial area index for highly sensitive diagnostic tests. Radiology, 201, 745–750.Google Scholar
Jiang, Y., Nishikawa, R.M., Schmidt, R.A., et al. (1999). Improving breast cancer diagnosis with computer-aided diagnosis. Acad Radiol, 6, 22.CrossRefGoogle ScholarPubMed
Jiang, Y., Nishikawa, R.M., Schmidt, R.A., et al. (2001). Potential of computer-aided diagnosis to reduce variability in radiologists’ interpretations of mammograms depicting microcalcifications. Radiology, 220, 787–794.CrossRefGoogle ScholarPubMed
Khoo, L.A.L., Taylor, P., Given-Wilson, R.M. (2005). Computer detection in the United Kingdom national breast screening programme: prospective study. Radiology, 237, 444–449.CrossRefGoogle ScholarPubMed
Krupinski, E.A., Kundel, H.L., Judy, P.F., et al. (1998). Key issues for image perception research. Radiology, 209, 611–612.Google Scholar
Kuhl, C.K., Mielcareck, P., Klaschik, S., et al. (1999). Dynamic breast MR imaging: are signal intensity time course data useful for differential diagnosis of enhancing lesions? Radiology, 211, 101–110.Google Scholar
Kundel, H. (1975). Peripheral vision, structured noise and film reader error. Radiology, 114, 269–273.Google Scholar
Kupinski, M.A., Giger, M.L. (1998). Automated seeded lesion segmentation on digital mammograms. IEEE Trans Med Imag, 17, 510–517.Google Scholar
Kupinski, M., Clarkson, E., Barrett, H. (2006). A probabilistic model for the MRMC method, part 2: validation and applications. Acad Radiol, 13, 1422–1430.CrossRefGoogle ScholarPubMed
McFarland, E.G., Brink, J.A., Pilgram, T.K., et al. (2001). Spiral CT colonography: reader agreement and diagnostic performance with two- and three-dimensional image-display techniques. Radiology, 218, 375–383.Google Scholar
Metz, C.E. (1978). Basic principles of ROC analysis. Semin Nucl Med, 8, 283–298.Google Scholar
Metz, C.E. (1986). ROC methodology in radiologic imaging. Invest Radiol, 21, 720–733.CrossRefGoogle ScholarPubMed
Metz, C.E. (2000). Fundamental ROC analysis. In: Beutel, J., Kundel, H., Van Metter, R. (eds.) Handbook of Medical Imaging, Volume 1. Physics and Psychophysics. Bellingham, WA: SPIE, pp. 751–769.Google Scholar
Morton, M.J., Whaley, D.H., Brandt, K.R., Amrami, K.K. (2006). Screening mammograms: interpretation with computer-aided detection – prospective evaluation. Radiology, 239, 375–383.Google Scholar
Muramatsu, C., Li, Q., Suzuki, K., et al. (2005). Investigation of psychophysical measures for evaluation of similar images for mammographic masses: preliminary results. Med Phys, 32, 2295–2304.Google Scholar
Nappi, J., Yoshida, H. (2003). Feature-guided analysis for reduction of false positives in CAD of polyps for computed tomographic colonography. Med Phys, 30, 1592–1601.CrossRefGoogle ScholarPubMed
Nishikawa, R.M., Giger, M.L., Vyborny, C.J., et al. (2001). Prospective computer analysis of cancers missed on screening mammography. In: Digital Mammography 2000, Proceedings of the 5th International Workshop on Digital Mammography. Madison, WI: Medical Physics, pp. 493–498.Google Scholar
Petrick, N., Haider, M., Summers, R.M., et al. (2008). CT colonography with computer-aided detection as a second reader: an observer performance study. Radiology, 246(1), 148–156.Google Scholar
Renfrew, D.L., Franken, E.A., Jr., Berbaum, K.S., Weigelt, F.H., AbuYousef, M.M. (1992). Error in radiology: classification and lessons in 182 cases presented at a problem case conference. Radiology, 183, 145–150.Google Scholar
Royster, A.P., Fenlon, H.M., Clarke, P.D., Nunes, D.P., Ferrucci, J.T. (1997). CT colonoscopy of colorectal neoplasms: two-dimensional and three-dimensional virtual-reality techniques with colonoscopic correlation. Am J Roentgenol, 169, 1237–1242.CrossRefGoogle ScholarPubMed
Sahiner, B., Chan, H.-P., Petrick, N., Wagner, R.F., Hadjiiski, L. (2000). Feature selection and classifier performance in computer-aided diagnosis: the effect of finite sample size. Med Phys, 27, 1509–1522.Google Scholar
Samuelson, F.W., Petrick, N. (2006). Comparing image detection algorithms using resampling. Proceedings of the 2006 IEEE International Symposium on Biomedical Imaging, pp. 1312–1315.Google Scholar
Shah, P.K., Austin, J.H., White, C.S., et al. (2003). Missed non-small cell lung cancer: radiographic findings of potentially resectable lesions evident only in retrospect. Radiology, 226, 235–241.Google Scholar
Sone, S., Li, F., Yang, Z.G., et al. (2001). Results of three-year mass screening programme for lung cancer using mobile low-dose spiral computed tomography scanner. Br J Cancer, 84, 25–32.Google Scholar
Sonka, M., Fitzpatrick, M.J. (eds.) (2000). Handbook of Medical Imaging, Volume 2. Medical Imaging Processing and Analysis. Bellingham, WA: SPIE.Google Scholar
Summers, R.M., Johnson, C.D., Pusanik, L.M., et al. (2001). Automated polyp detection at CT colonography: feasibility assessment in a human population. Radiology, 219, 51–59.Google Scholar
Summers, R.M., Yao, J., Pickhardt, P.J., et al. (2005). Computed tomographic virtual colonoscopy computer-aided polyp detection in a screening population. Gastroenterology, 129, 1832–1844.Google Scholar
Suzuki, K., Yoshida, H., Nappi, J., Dachman, A.H. (2006). Massive-training artificial neural network (MTANN) for reduction of false positives in computer-aided detection of polyps: suppression of rectal tubes. Med Phys, 33, 3814–3824.Google Scholar
Swensen, S.J., Jett, J.R., Hartman, T.E., et al. (2003). Lung cancer screening with CT: Mayo Clinic experience. Radiology, 226, 756–761.Google Scholar
Taylor, S.A., Charman, S.C., Lefere, P., et al. (2007). CT colonography: investigation of the optimum reader paradigm by using computer-aided detection software. Radiology, 246(2), 463–471.Google Scholar
Wagner, R.F., Metz, C.E., Campbell, G. (2007). Assessment of medical imaging systems and computer aids: a tutorial review. Acad Radiol, 14, 723–748.Google Scholar
Yoshida, H., Dachman, A. (2004). Computer-aided diagnosis for CT colonography. Semin Ultrasound CT MR, 25, 419–431.CrossRefGoogle ScholarPubMed
Yoshida, H., Masutani, Y., MacEneaney, P., Rubin, D.T., Dachman, A.H. (2002). Computerized detection of colonic polyps at CT colonography on the basis of volumetric features: pilot study. Radiology, 222: 327–336.Google Scholar
Yousef, W.A., Wagner, R.F., Loew, M.H. (2005). Estimating the uncertainty in the estimated mean area under the ROC curve of a classifier. Patt Recog Lett, 26, 2600–2610.Google Scholar
Yousef, W.A., Wagner, R.F., Loew, M.H. (2006). Assessing classifiers from two independent data sets using ROC analysis: a nonpara-metric approach. IEEE Trans Patt Anal Mach Intell, 28, 1809–1817.Google Scholar
Yuan, Y., Giger, M.L., Li, H., Suzuki, K., Sennett, C. (2007). A dual-stage method for lesion segmentation on digital mammograms. Med Phys, 34, 4180–4193.Google Scholar
Zheng, B., Ganott, M.A., Britton, C.A., et al. (2001). Soft-copy mammographic reading with different computer-assisted detection cuing environments: preliminary findings. Radiology, 221, 633–640.Google Scholar
Zheng, B., Swensson, R.G., Golla, S., et al. (2004). Detection and classification performance levels of mammographic masses under different computer-aided detection cueing environments. Acad Radiol, 11, 396–406.Google Scholar
Zheng, B., Mello-Thoms, C.C., Wang, X.-H., et al. (2007). Interactive computer-aided diagnosis of breast masses: computerized selection of visually similar image sets from a reference library. Acad Radiol, 14, 917–927.Google Scholar

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