Published Online:https://doi.org/10.1148/radiol.11091710

If it is easy to work on computer-aided diagnosis (CAD) development, because an accessible system to validate and combine algorithms has been created, and if high performing systems that will surface can be easily plugged into all clinical viewing software, then CAD will thrive.

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 workflow integration, regulatory approval, and cost efficiency. Performance is still the major bottleneck for many CAD systems. Novel ways of using 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.

© RSNA, 2011

References

  • 1 Rubin GD. Data explosion: the challenge of multidetector-row CT. Eur J Radiol 2000;36(2):74–80.
  • 2 Giger ML, Karssemeijer N, Armato SG. Computer-aided diagnosis in medical imaging. IEEE Trans Med Imaging 2001;20(12):1205–1208.
  • 3 Moore’s Law. http://en.wikipedia.org/wiki/Moore’s_law. Accessed August 26, 2009.
  • 4 Kurzweil R. The singularity is near: when humans transcend biology. New York, NY: Viking Penguin, 2005.
  • 5 Lodwick GS, Keats TE, Dorst JP. The coding of Roentgen images for computer analysis as applied to lung cancer. Radiology 1963;81:185–200.
  • 6 Lodwick GS. Computer-aided diagnosis in radiology. A research plan. Invest Radiol 1966;1(1):72–80.
  • 7 De Boo DW, Prokop M, Uffmann M, van Ginneken B, Schaefer-Prokop CM. Computer-aided detection (CAD) of lung nodules and small tumours on chest radiographs. Eur J Radiol 2009;72(2):218–225.
  • 8 Jain AK. Fundamentals of digital image processing. Upper Saddle River, NJ: Prentice Hall, 1989.
  • 9 Duda RO, Hart PE, Stork DG. Pattern classification. 2nd ed. New York, NY: Wiley, 2001.
  • 10 Bishop CM. Pattern recognition and machine learning. Secaucus, NJ: Springer, 2007.
  • 11 Duin RP. Superlearning and neural network magic. Pattern Recognit Lett 1994;15(3):215–217.
  • 12 Bennett KP. Support vector machines: hype or hallelujah? SIGKDD Explor Newslett 2000;2(2):1–13.
  • 13 van Ginneken B, Hogeweg L, Prokop M. Computer-aided diagnosis in chest radiography: beyond nodules. Eur J Radiol 2009;72(2):226–230.
  • 14 Giger ML, Chan HP, Boone J. Anniversary paper: History and status of CAD and quantitative image analysis: the role of Medical Physics and AAPM. Med Phys 2008;35(12):5799–5820.
  • 15 Doi K. Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Comput Med Imaging Graph 2007;31(4-5):198–211.
  • 16 Nishikawa RM. Current status and future directions of computer-aided diagnosis in mammography. Comput Med Imaging Graph 2007;31(4-5):224–235.
  • 17 van Ginneken B, ter Haar Romeny BM, Viergever MA. Computer-aided diagnosis in chest radiography: a survey. IEEE Trans Med Imaging 2001;20(12):1228–1241.
  • 18 Sluimer IC, Schilham AM, Prokop M, van Ginneken B. Computer analysis of computed tomography scans of the lung: a survey. IEEE Trans Med Imaging 2006;25(4):385–405.
  • 19 Chan HP, Hadjiiski L, Zhou C, Sahiner B. Computer-aided diagnosis of lung cancer and pulmonary embolism in computed tomography-a review. Acad Radiol 2008;15(5):535–555.
  • 20 Boyer B, Balleyguier C, Granat O, Pharaboz C. CAD in questions/answers: review of the literature. Eur J Radiol 2009;69(1):24–33.
  • 21 Roehrig J. The manufacturer’s perspective. Br J Radiol 2005;78(Spec No 1):S41–S45.
  • 22 Dean JC, Ilvento CC. Improved cancer detection using computer-aided detection with diagnostic and screening mammography: prospective study of 104 cancers. AJR Am J Roentgenol 2006;187(1):20–28.
  • 23 Brem RF. Clinical versus research approach to breast cancer detection with CAD: where are we now? AJR Am J Roentgenol 2007;188(1):234–235.
  • 24 Noble M, Bruening W, Uhl S, Schoelles K. Computer-aided detection mammography for breast cancer screening: systematic review and meta-analysis. Arch Gynecol Obstet 2009;279(6):881–890.
  • 25 Aunt Minnie Buyer’s Guide. http://www.auntminnie.com/index.asp?sec=vdp. Accessed August 26, 2009.
  • 26 Das M, Mühlenbruch G, Mahnken AH, et al.. Small pulmonary nodules: effect of two computer-aided detection systems on radiologist performance. Radiology 2006;241(2):564–571.
  • 27 CDRH Advisory Meeting Materials Archive. http://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfAdvisory/details.cfm?mtg=659. Accessed August 26, 2009.
  • 28 Gur D. Imaging technology and practice assessment studies: importance of the baseline or reference performance level. Radiology 2008;247(1):8–11.
  • 29 Horsch K, Giger ML, Vyborny CJ, Lan L, Mendelson EB, Hendrick RE. Classification of breast lesions with multimodality computer-aided diagnosis: observer study results on an independent clinical data set. Radiology 2006;240(2):357–368.
  • 30 Karssemeijer N, Otten JD, Rijken H, Holland R. Computer aided detection of masses in mammograms as decision support. Br J Radiol 2006;79(Spec No 2):S123–S126.
  • 31 Abràmoff MD, Niemeijer M, Suttorp-Schulten MS, Viergever MA, Russell SR, van Ginneken B. Evaluation of a system for automatic detection of diabetic retinopathy from color fundus photographs in a large population of patients with diabetes. Diabetes Care 2008;31(2):193–198.
  • 32 Nishikawa RM. Computer-aided detection, in its present form, is not an effective aid for screening mammography. For the proposition. Med Phys 2006;33(4):811–812.
  • 33 Jiang Y, Nishikawa RM, Schmidt RA, Toledano AY, Doi K. Potential of computer-aided diagnosis to reduce variability in radiologists’ interpretations of mammograms depicting microcalcifications. Radiology 2001;220(3):787–794.
  • 34 Boone JM. Radiological interpretation 2020: toward quantitative image assessment. Med Phys 2007;34(11):4173–4179.
  • 35 Isgum I, Rutten A, Prokop M, van Ginneken B. Detection of coronary calcifications from computed tomography scans for automated risk assessment of coronary artery disease. Med Phys 2007;34(4):1450–1461.
  • 36 Rumberger JA. Coronary artery calcium scanning using computed tomography: clinical recommendations for cardiac risk assessment and treatment. Semin Ultrasound CT MR 2008;29(3):223–229.
  • 37 Müller NL, Staples CA, Miller RR, Abboud RT. “Density mask”. An objective method to quantitate emphysema using computed tomography. Chest 1988;94(4):782–787.
  • 38 Xu Y, Sonka M, McLennan G, Guo J, Hoffman EA. MDCT-based 3-D texture classification of emphysema and early smoking related lung pathologies. IEEE Trans Med Imaging 2006;25(4):464–475.
  • 39 Sluimer IC, Prokop M, Hartmann I, van Ginneken B. Automated classification of hyperlucency, fibrosis, ground glass, solid, and focal lesions in high-resolution CT of the lung. Med Phys 2006;33(7):2610–2620.
  • 40 Tanner JM, Oshman D, Lindgren G, Grunbaum JA, Elsouki R, Labarthe D. Reliability and validity of computer-assisted estimates of Tanner-Whitehouse skeletal maturity (CASAS): comparison with the manual method. Horm Res 1994;42(6):288–294.
  • 41 Albanese A, Hall C, Stanhope R. The use of a computerized method of bone age assessment in clinical practice. Horm Res 1995;44(Suppl 3):2–7.
  • 42 Thodberg HH, Kreiborg S, Juul A, Pedersen KD. The BoneXpert method for automated determination of skeletal maturity. IEEE Trans Med Imaging 2009;28(1):52–66.
  • 43 Greulich WW, Pyle SI. Radiographic atlas of skeletal development of the hand and wrist. 2nd ed. Stanford, Calif: Stanford University Press, 1959.
  • 44 van Rijn RR, Lequin MH, Thodberg HH. Automatic determination of Greulich and Pyle bone age in healthy Dutch children. Pediatr Radiol 2009;39(6):591–597.
  • 45 Martin DD, Deusch D, Schweizer R, Binder G, Thodberg HH, Ranke MB. Clinical application of automated Greulich-Pyle bone age determination in children with short stature. Pediatr Radiol 2009;39(6):598–607.
  • 46 de Hoop B, Gietema H, van Ginneken B, Zanen P, Groenewegen G, Prokop M. A comparison of six software packages for evaluation of solid lung nodules using semiautomated volumetry: what is the minimum increase in size to detect growth in repeated CT examinations. Eur Radiol 2009;19(4):800–808.
  • 47 Boedeker KL, McNitt-Gray MF, Rogers SR, et al.. Emphysema: effect of reconstruction algorithm on CT imaging measures. Radiology 2004;232(1):295–301.
  • 48 Schilham AM, van Ginneken B, Gietema H, Prokop M. Local noise weighted filtering for emphysema scoring of low-dose CT images. IEEE Trans Med Imaging 2006;25(4):451–463.
  • 49 Gietema HA, Schilham AM, van Ginneken B, van Klaveren RJ, Lammers JW, Prokop M. Monitoring of smoking-induced emphysema with CT in a lung cancer screening setting: detection of real increase in extent of emphysema. Radiology 2007;244(3):890–897.
  • 50 Wormanns D, Kohl G, Klotz E, et al.. Volumetric measurements of pulmonary nodules at multi-row detector CT: in vivo reproducibility. Eur Radiol 2004;14(1):86–92.
  • 51 Detrano RC, Anderson M, Nelson J, et al.. Coronary calcium measurements: effect of CT scanner type and calcium measure on rescan reproducibility—MESA study. Radiology 2005;236(2):477–484.
  • 52 Heussel CP, Achenbach T, Buschsieweke C, et al.. Quantification of pulmonary emphysema in multislice-CT using different software tools [in German]. Rofo 2006;178(10):987–998.
  • 53 Summers RM. Road maps for advancement of radiologic computer-aided detection in the 21st century. Radiology 2003;229(1):11–13.
  • 54 Noumeir R. Benefits of the DICOM structured report. J Digit Imaging 2006;19(4):295–306.
  • 55 Zhou Z, Liu BJ, Le AH. CAD-PACS integration tool kit based on DICOM secondary capture, structured report and IHE workflow profiles. Comput Med Imaging Graph 2007;31(4-5):346–352.
  • 56 Integrating the Healthcare Enterprise. http://www.ihe.net/. Accessed September 6, 2009.
  • 57 van Ginneken B, Stegmann MB, Loog M. Segmentation of anatomical structures in chest radiographs using supervised methods: a comparative study on a public database. Med Image Anal 2006;10(1):19–40.
  • 58 MacMahon H, Montner SM, Doi K, Liu KJ. The nature and subtlety of abnormal findings in chest radiographs. Med Phys 1991;18(2):206–210.
  • 59 Simon HA, Bibel W, Bundy A. AI’s greatest trends and controversies. IEEE Intell Syst 2000;15(1):8–17.
  • 60 Turing AM. Can a machine think? Mind 1950;59(236):433–460.
  • 61 Lohr S. Microsoft, amid dwindling interest, talks up computing as a career. New York Times, March 1, 2004.
  • 62 Li F, Engelmann R, Doi K, Macmahon H. True detection versus “accidental” detection of small lung cancer by a computer-aided detection (CAD) program on chest radiographs. J Digit Imaging 2010;23(1):66–72.
  • 63 Maglogiannis I, Doukas CN. Overview of advanced computer vision systems for skin lesions characterization. IEEE Trans Inf Technol Biomed 2009;13(5):721–733.
  • 64 Sonka M, Hlavác V, Boyle R. Image processing, analysis, and machine vision. 3rd ed. Toronto, Canada: Thomson Learning, 2007.
  • 65 Karssemeijer N, Otten JD, Verbeek AL, et al.. Computer-aided detection versus independent double reading of masses on mammograms. Radiology 2003;227(1):192–200.
  • 66 Armato SG, McLennan G, McNitt-Gray MF, et al.. Lung image database consortium: developing a resource for the medical imaging research community. Radiology 2004;232(3):739–748.
  • 67 National Biomedical Imaging Archive. http://ncia.nci.nih.gov/. Accessed August 26, 2009.
  • 68 van Ginneken B, Heimann T, Styner M. 3D segmentation in the clinic: a grand challenge. In: van Ginneken BHeimann TStyner M, eds. 3D segmentation in the clinic: a grand challenge. Brisbane, Australia: MICCAI, 2007; 7–15.
  • 69 Heimann T, van Ginneken B, Styner MA, et al.. Comparison and evaluation of methods for liver segmentation from CT datasets. IEEE Trans Med Imaging 2009;28(8):1251–1265.
  • 70 Schaap M, Metz CT, van Walsum T, et al.. Standardized evaluation methodology and reference database for evaluating coronary artery centerline extraction algorithms. Med Image Anal 2009;13(5):701–714.
  • 71 The ANODE09 Competition for Nodule Detection in Chest CT. http://anode09.isi.uu.nl. Accessed August 26, 2009.
  • 72 Volcano’09 Challenge. http://www.via.cornell.edu/challenge/. Accessed August 26, 2009.
  • 73 Retinopathy online challenge. http://roc.healthcare.uiowa.edu/. Accessed August 26, 2009.
  • 74 Netflix Prize. http://www.netflixprize.com/. Accessed August 26, 2009.
  • 75 Ellenberg J This psychologist might outsmart the math brains competing for the Netflix prize. Wired, February 2008.
  • 76 Surowiecki JM. The wisdom of crowds: why the many are smarter than the few and how collective wisdom shapes business, economies, societies and nations. New York, NY: Little, Brown, 2004.
  • 77 Reitinger B, Bornik A, Beichel R, Schmalstieg D. Liver surgery planning using virtual reality. IEEE Comput Graph Appl 2006;26(6):36–47.
  • 78 De Hoop B, De Boo D, Gietema HA, Van Hoorn F, Mearadji B, Schaefer-Prokop CM. CAD for the detection of tumors in CXRs of smokers: results of an observer L-ROC study [abstr]. In: Radiological Society of North America Scientific Assembly and Annual Meeting Program. Oak Brook, Ill: Radiological Society of North America, 2009; 533–534.
  • 79 Kittler J, Hatef M, Duin RP, Matas J. On combining classifiers. IEEE Trans Pattern Anal Mach Intell 1998;20(3):226–239.
  • 80 Karssemeijer N, Bluekens AM, Beijerinck D, et al.. Breast cancer screening results 5 years after introduction of digital mammography in a population-based screening program. Radiology 2009;253(2):353–358.
  • 81 Lee N, Lain AF, Marquez G, Levsky JM, Gohagan JK. Potential of computer-aided diagnosis to improve CT lung cancer screening. IEEE Rev Biomed Eng 2009;2:136–146.
  • 82 Robinson C, Halligan S, Taylor SA, Mallett S, Altman DG. CT colonography: a systematic review of standard of reporting for studies of computer-aided detection. Radiology 2008;246(2):426–433.
  • 83 Halligan S, Altman DG, Mallett S, et al.. Computed tomographic colonography: assessment of radiologist performance with and without computer-aided detection. Gastroenterology 2006;131(6):1690–1699.
  • 84 Bell RM, Koren Y. Lessons from the Netflix prize challenge. SIGKDD Explor Newslett 2007;9(2):1931–1945.
  • 85 Bell RM, Koren Y. Scalable collaborative filtering with jointly derived neighborhood interpolation weights. Seventh IEEE International Conference on Data Mining, 2007; 43–52.

Article History

Received September 4, 2009; revision requested October 22; revision received April 12, 2010; accepted April 20; final version accepted May 19; final review by B.v.G. July 25, 2011.
Published online: Dec 2011
Published in print: Dec 2011