Abstract
The quantity of digital medical images that must be reviewed by radiologists as part of routine clinical practice has greatly increased in recent years. New acquisition devices generate images that have higher spatial resolution, both in 2–D as well as 3–D, requiring physicians to use more sophisticated visualization tools. In addition, advanced visualization systems, designed to assist the radiologist, are now part of a standard arsenal of tools which, together with workflow improvements, aid the physicians in their clinical tasks. Computer–Assisted Diagnosis (CAD) systems are one of such class of sophisticated tools to support the radiologists in tedious and time–consuming tasks such as the detection of lesions. Over the past ten years, CAD systems have evolved to reach sensitivity capabilities equivalent to or exceeding that of a radiologist, thus becoming clinically acceptable, but with limited specificity which necessitates their use as a second reader tool. This chapter presents one such system (LungCAD) designed for the detection of nodules in the lung parenchyma. Its performance was evaluated as part of a detection challenge organized by ImageCLEF 2009.
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References
Armato III S, Giger M, Moran C, Blackburn J, Doi K, MacMahon H (1999) Computerized detection of pulmonary nodules on CT scans. RadioGraphics 19:1303–1311
Armato III S, Roberts R, Kocherginsky M, Aberle D, Kazerooni E, MacMahon H, van Beek E, Yankelevitz D, McLennan G, McNitt-Gray M, Meyer C, Reeves A, Caligiuri P, Quint L, Sundaram B, Croft B, Clarke L (2009) Assessment of radiologist performance in the detection of lung nodules: dependence on the definition of truth. Academic Radiology 16:28–38
Beyer F, Zierott L, Fallenberg EM, Juergens KU, Stoeckel J, Heindel W, Wormanns D (2007) Comparison of sensitivity and reading time for the use of computer-aided detection (cad) of pulmonary nodules at MDCT as concurrent or second reader. European Radiology 17(11):2941–2947
Bogoni L, Liang J, Periaswamy S (2009) System and method for toboggan based object segmentation using divergent gradient field response in images. Technical Report US7526115B2, Siemens Medical solutions USA, Inc., Malvern, PA
Chang S, Emoto H, Metaxas DN, Axel L (2004) Pulmonary micronodule detection from 3D Chest CT. Lecture Notes in Computer Sciences (LNCS) 3217:821–828
Clarke L, Croft B (2004) Development of public resources to support quantitative imaging methods in cancer. Academic Radiology 14:1438–1440
Dundar MM, Wolf M, Lakare S, Salganicoff M, Raykar VC (2008) Polyhedral classifier for target detection: a case study: colorectal cancer. In: Proceedings of the 25th international conference on Machine learning. ACM press, pp 288–295
Farag AA, El-Baz A, Gimel’farb GG, Falk R, Hushek SG (2004) Automatic detection and recognition of lung abnormalities in helical ct images using deformable templates. Lecture Notes in Computer Science (LNCS) 3217:856–864
Godoy MC, Naidich DP (2009) Subsolid pulmonary nodules and the spectrum of peripheral adenocarcinomas of the lung: Recommended interim guidelines for assessment and management. Radiology 606–622
Godoy MC, Kim TJ, Ko JP, Florin CH, Jerebko AK, Naidich DP (2008) Computer-aided detection of pulmonary nodules on CT: Evaluation of a new prototype for detection of ground–glass and part–solid nodules. In: Proceedings of the RSNA
Godoy MC, Bonavita J, Girvin F, O’Sullivan P, Wickstrom M, Naidich D (2009a) Role of a computer–assisted diagnosis (cad) within pacs for identifying nodules on low dose screening ct studies: A prospective evaluation. In: Proceedings of the RSNA
Godoy MC, Ko JP, Kim TJ, Naidich DP, Bogoni L, Florin CH, Vlahos I, Park S, Salganicoff M (2009b) Effect of computer–aided diagnosis on radiologists’ detection performance of subsolid pulmonary nodules on ct: Initial results. In: Proceedings of the American Roentgen Ray Society annual meeting
Hounsfield GN (1973) Computerised transverse axial scanning (tomography) part 1: Description of system. British Journal of Radiology 46:1016–1022
Jemal A, Siegel R, Ward E, Hao Y, Xu J, Murray T, Thun MJ (2008) Cancer statistics, 2008. CA: A Cancer Journal for Clinicians 58(2):71–96
Ko JP, Betke M (2001) Chest ct: Automated nodule detection and assessment of change over time-preliminary experience. Radiology 218(1):267–273
Lee Y, Hara T, Fujita H, Itoh S, Ishigaki T (2001) Automated detection of pulmonary nodules in helical ct images based on an improved template-matching technique. IEEE Transactions of Medical Imaging 20(7):595–604
MacMahon H, Austin JHM, Gamsu G, Herold CJ, Jett JR, Naidich DP, Patz Jr EF, Swensen SJ (2005) Guidelines for management of small pulmonary nodules detected on ct scans: A statement from the fleischner society. Radiology 395–400
Mani A, Napel S, Paik D, Jeffrey RJ, Yee J, Olcott E, Prokesch R, Davila M, Schraedley-Desmond P, Beaulieu C (2004) Computed tomography colonography: feasibility of computer–aided polyp detection in a "first reader" paradigm. Journal of Computer Assisted Tomography 28(3):318–326
McClish D, Zhou X, Obuchowski N (2002) Statistical methods in diagnostic medicine. Wiley Series in Probability and Statistics, vol 414. Wiley, p 464
McNitt-Gray MF, Armato SG, Meyer CR, Reeves AP, McLennan G, Pais RC, Freymann J, Brown MS, Engelmann RM, Bland PH, Laderach GE, Piker C, Guo J, Towfic Z, Qing DPY, Yankelevitz DF, Aberle DR, van Beek EJR, MacMahon H, Kazerooni EA, Croft BY, Clarke LP (2007) The lung image database consortium (lidc) data collection process for nodule detection and annotation. Academic Radiology 14(12):1464–1474
Naidich DP, Ko JP, Stoeckel J, Abinanti N, Lu S, Moses D, Moore W, Vlahos I, Novak CL (2004) Computer–aided diagnosis: impact on nodule detection among community level radiologists. a multi-reader study. In: CARS, pp 902–907
Opfer R, Wiemker R (2007) Performance analysis for computer-aided lung nodule detection on lidc data. Proceedings of the SPIE 6515:65151
Paik DS, Beaulieu CF, Rubin GD, Acar B, Jeffrey Jr RB, Yee J, Dey J, Napel S (2004) Surface normal overlap: A computer-aided detection algorithm with application to colonic polyps and lung nodules in helical ct. IEEE Transactions on Medial Imaging 23(6):661–675
Rao RB, Fung G, Krishnapuram B, Bi J, Dundar M, Raykar VC, Yu S, Krishnan S, Zhou X, Krishnan A, Salganicoff M, Bogoni L, Wolf M, Jerebko A, Stoeckel J (2009) Mining medical images. In Proceedings of the Third Workshop on Data Mining Case Studies and Practice Prize, Fifteenth Annual SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2009)
Raykar V, Krishnapuram B, Bi J, Dundar M, Bharat R (2008) Bayesian multiple instance learning: Automatic feature selection and inductive transfer. In Proceedings of the 25th International Conference on Machine Learning 307:808–815
Raykar VC, Yu S, Zhao LH, Valadez GH, Florin C, Bogoni L, Moy L (In Press) Learning from crowds. Journal of Machine Learning Research
Summers RM (2003) Road maps for advancement of radiologic computer-aided detection in the 21st century. Radiology 229(1):11–13
of Surveillance D, Research HP (2009) Cancer facts & figures. American Cancer Society
Suzuki K, Asamura H, Kusumoto M, Kondo H, Tsuchiya R (2002) “Early” peripheral lung cancer: prognostic significance of ground glass opacity on thin-section computed tomographic scan. Annals of Thoracic Surgery 74(5):1635–1639
Suzuki K, Li F, Sone S, Doi K (2005) Computer–aided diagnostic scheme for distinction between benign and malignant nodules in thoracic low–dose CT by use of massive training artificial neural network. IEEE Transactions of Medical Imaging 24(9):1138–1150
Tao Y, Zhou XS, Bi J, Jerebko A, Wolf M, Salganicoff M, Krishnan A (2009) An adaptive, knowledge-driven medical image search engine for interactive diffuse parenchymal lung disease quantification. In: SPIE proceedings, vol 7260
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Bogoni, L. et al. (2010). Lung Nodule Detection. In: Müller, H., Clough, P., Deselaers, T., Caputo, B. (eds) ImageCLEF. The Information Retrieval Series, vol 32. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15181-1_22
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DOI: https://doi.org/10.1007/978-3-642-15181-1_22
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