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Computer-Aided Detection of Lung Cancer

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Image-Based Computer-Assisted Radiation Therapy

Abstract

As medical imaging technologies advance, a large number of medical images are produced which physicians/radiologists must interpret. Consequently, computer aids are becoming indispensable in physicians’ decision-making based on medical images. Computer-aided diagnosis (CAD) has been investigated and becomes an active research area in medical imaging. CAD is defined as detection and/or diagnosis made by a radiologist/physician who takes into account the computer output as a “second opinion.” In CAD research, detection of lung cancer in thoracic imaging constitutes a major research area, because lung cancer is the leading cause of cancer death worldwide, including the United States, Japan, and other countries. In this chapter, CAD for the detection of lung cancer in thoracic computed tomography (CT) is overviewed with emphasis on machine learning that plays an essential role in CAD systems. Massive training artificial neural network (MTANN) technology is one of the most promising machine learning techniques in image analysis. The MTANNs have substantially improved the sensitivity and specificity of CAD systems in detection and diagnosis of lung cancer. MTANN CAD systems offer high performance in detection and diagnosis of lung cancer in CT. Thus, MTANN CAD systems would be useful for improving the diagnostic performance of radiologists/physicians in early detection of lung cancer.

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References

  • Aoyama M, Li Q, Katsuragawa S, MacMahon H, Doi K (2002) Automated computerized scheme for distinction between benign and malignant solitary pulmonary nodules on chest images. Med Phys 29:701–708

    Article  PubMed  Google Scholar 

  • Arimura H, Katsuragawa S, Suzuki K, Li F, Shiraishi J, Sone S, Doi K (2004) Computerized scheme for automated detection of lung nodules in low-dose computed tomography images for lung cancer screening. Acad Radiol 11:617–629

    Article  PubMed  Google Scholar 

  • Armato SG 3rd, Giger ML, Moran CJ, Blackburn JT, Doi K, MacMahon H (1999) Computerized detection of pulmonary nodules on CT scans. Radiographics 19:1303–1311

    Google Scholar 

  • Armato SG 3rd, Giger ML, MacMahon H (2001) Automated detection of lung nodules in CT scans: preliminary results. Med Phys, 28:1552–1561

    Google Scholar 

  • Armato SG 3rd, Mclennan G, Mcnitt-Gray MF, Meyer CR, Yankelevitz D, Aberle DR, Henschke CI, Hoffman EA, Kazerooni EA, MacMahon H, Reeves AP, Croft BY, Clarke LP (2004) Lung image database consortium: developing a resource for the medical imaging research community. Radiology 232:739–748

    Google Scholar 

  • Bae KT, Kim JS, Kim JH, Na Y (2002) Automatic detection of pulmonary nodules in multi-slice CT: performance of 3D morphologic matching algorithm. Radiology 225(P):476

    Google Scholar 

  • Becker HC, Nettleton WJ Jr, Meyers PH, Sweeney JW, Nice Jr CM (1964) Digital computer determination of a medical diagnostic index directly from chest X-Ray images. IEEE Trans Biomed Eng 11:67–72

    Google Scholar 

  • Chan HP, Doi K, Galhotra S, Vyborny CJ, MacMahon H, Jokich PM (1987) Image feature analysis and computer-aided diagnosis in digital radiography. I. Automated detection of microcalcifications in mammography. Med Phys 14:538–548

    Article  CAS  PubMed  Google Scholar 

  • Chan HP, Sahiner B, Wagner RF, Petrick N (1999) Classifier design for computer-aided diagnosis: effects of finite sample size on the mean performance of classical and neural network classifiers. Med Phys 26:2654–2668

    Article  CAS  PubMed  Google Scholar 

  • Chen H, Xu Y, Ma Y, Ma B (2010) Neural network ensemble-based computer-aided diagnosis for differentiation of lung nodules on CT images: clinical evaluation. Acad Radiol 17:595–602

    Google Scholar 

  • Chen S, Suzuki K (2013) Computerized detection of lung nodules by means of “virtual dual-energy” radiography. IEEE Trans Biomed Eng 60:369–378

    Article  PubMed  Google Scholar 

  • Chen S, Suzuki K (2014) Separation of bones from chest radiographs by means of anatomically specific multiple massive-training ANNs combined with total variation minimization smoothing. IEEE Trans Med Imaging 33:246–257

    Article  Google Scholar 

  • Chen S, Zhong S, Yao L, Shang Y, Suzuki K (2016) Enhancement of chest radiographs obtained in the intensive care unit through bone suppression and consistent processing. Phys Med Biol 61:2283–2301

    Article  PubMed  Google Scholar 

  • Doi K (2005) Current status and future potential of computer-aided diagnosis in medical imaging. Br J Radiol 78(Spec No 1):S3–S19

    Google Scholar 

  • Doi K (2007) Computer-aided diagnosis in medical imaging: Historical review, current status and future potential. Comput Med Imaging Graph 31:198–211

    Article  PubMed  PubMed Central  Google Scholar 

  • Duda RO, Hart PE, Stork DG (2001) Pattern recognition, 2nd edn. Wiley Interscience, Hoboken

    Google Scholar 

  • Edwards DC, Kupinski MA, Metz CE, Nishikawa RM (2002) Maximum likelihood fitting of FROC curves under an initial-detection-and-candidate-analysis model. Med Phys 29:2861–2870

    Article  PubMed  Google Scholar 

  • Farag AA, EL-Baz A, Gimelfarb G, EL-Ghar MA, Eldiasty T (2005) Quantitative nodule detection in low dose chest CT scans: new template modeling and evaluation for CAD system design. Med Image Comput Comput Assist Interv 8:720–728

    PubMed  Google Scholar 

  • Frangi AF, Niessen WJ, Hoogeveen RM, Van Walsum T, Viergever MA (1999) Model-based quantitation of 3-D magnetic resonance angiographic images. IEEE Trans Med Imaging 18:946–956

    Article  CAS  PubMed  Google Scholar 

  • Fujita H, Doi K, Fencil LE, Chua KG (1987) Image feature analysis and computer-aided diagnosis in digital radiography. 2. Computerized determination of vessel sizes in digital subtraction angiography. Med Phys 14:549–556

    Article  CAS  PubMed  Google Scholar 

  • Fukunaga K (1990) Introduction to statistical pattern recognition. Academic, San Diego

    Google Scholar 

  • Ge Z, Sahiner B, Chan HP, Hadjiiski LM, Cascade PN, Bogot N, Kazerooni EA, Wei J, Zhou C (2005) Computer-aided detection of lung nodules: false positive reduction using a 3D gradient field method and 3D ellipsoid fitting. Med Phys 32:2443–2454

    Article  PubMed  PubMed Central  Google Scholar 

  • Giger ML, Suzuki K (2007) Computer-Aided Diagnosis (CAD). In: Feng DD (ed) biomedical information technology. Academic

    Google Scholar 

  • Giger ML, Doi K, MacMahon H (1988) Image feature analysis and computer-aided diagnosis in digital radiography. 3. Automated detection of nodules in peripheral lung fields. Med Phys 15:158–166

    Article  CAS  PubMed  Google Scholar 

  • Giger ML, Bae KT, MacMahon H (1994) Computerized detection of pulmonary nodules in computed tomography images. Investig Radiol 29:459–465

    Article  CAS  Google Scholar 

  • Giger ML, Chan HP, Boone J (2008) Anniversary paper: history and status of CAD and quantitative image analysis: the role of Medical Physics and AAPM. Med Phys 35:5799–5820

    Article  PubMed  PubMed Central  Google Scholar 

  • Golosio B, Masala GL, Piccioli A, Oliva P, Carpinelli M, Cataldo R, Cerello P, De Carlo F, Falaschi F, Fantacci ME, Gargano G, Kasae P, Torsello M (2009) A novel multithreshold method for nodule detection in lung CT. Med Phys 36:3607–3618

    Article  PubMed  Google Scholar 

  • Gurcan MN, Sahiner B, Petrick N, Chan HP, Kazerooni EA, Cascade PN, Hadjiiski L (2002) Lung nodule detection on thoracic computed tomography images: preliminary evaluation of a computer-aided diagnosis system. Med Phys 29:2552–2558

    Article  PubMed  Google Scholar 

  • Hanley JA, Mcneil BJ (1983) A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology 148:839–843

    Article  CAS  PubMed  Google Scholar 

  • Hanson AJ (1992) The rolling ball. Graphics Gems III. Academic, Cambridge, MA

    Google Scholar 

  • He L, Chao Y, Suzuki K, Wu K (2009) Fast connected-component labeling. Pattern Recogn 42:1977–1987

    Article  Google Scholar 

  • Henschke CI, Mccauley DI, Yankelevitz DF, Naidich DP, Mcguinness G, Miettinen OS, Libby DM, Pasmantier MW, Koizumi J, Altorki NK, Smith JP (1999) Early Lung Cancer Action Project: overall design and findings from baseline screening. Lancet 354:99–105

    Article  CAS  PubMed  Google Scholar 

  • Henschke CI, Yankelevitz DF, Naidich DP, Mccauley DI, Mcguinness G, Libby DM, Smith JP, Pasmantier MW, Miettinen OS (2004) CT screening for lung cancer: suspiciousness of nodules according to size on baseline scans. Radiology 231:164–168

    Article  PubMed  Google Scholar 

  • Iwano S, Nakamura T, Kamioka Y, Ikeda M, Ishigaki T (2008) Computer-aided differentiation of malignant from benign solitary pulmonary nodules imaged by high-resolution CT. Comput Med Imaging Graph 32:416–422

    Article  PubMed  Google Scholar 

  • Kaneko M, Eguchi K, Ohmatsu H, Kakinuma R, Naruke T, Suemasu K, Moriyama N (1996) Peripheral lung cancer: screening and detection with low-dose spiral CT versus radiography. Radiology 201:798–802

    Article  CAS  PubMed  Google Scholar 

  • Katsuragawa S, Doi K, MacMahon H (1988) Image feature analysis and computer-aided diagnosis in digital radiography: detection and characterization of interstitial lung disease in digital chest radiographs. Med Phys 15:311–319

    Article  CAS  PubMed  Google Scholar 

  • Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Proces Syst 1097-1105

    Google Scholar 

  • Lecun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444

    Article  CAS  PubMed  Google Scholar 

  • 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 Trans Med Imaging 20:595–604

    Article  CAS  PubMed  Google Scholar 

  • Lee MC, Boroczky L, Sungur-Stasik K, Cann AD, Borczuk AC, Kawut SM, Powell CA (2010) Computer-aided diagnosis of pulmonary nodules using a two-step approach for feature selection and classifier ensemble construction. Artif Intell Med 50:43–53

    Article  PubMed  Google Scholar 

  • Li F, Sone S, Abe H, MacMahon H, Armato SG 3rd, Doi K (2002) Lung cancers missed at low-dose helical CT screening in a general population: comparison of clinical, histopathologic, and imaging findings. Radiology 225(3):673–683

    Article  PubMed  Google Scholar 

  • Li Q, Sone S, Doi K (2003) Selective enhancement filters for nodules, vessels, and airway walls in two- and three-dimensional CT scans. Med Phys 30:2040–2051

    Article  PubMed  Google Scholar 

  • Lusted LB (1955) Medical electronics. N Engl J Med 252:580–585

    Article  CAS  PubMed  Google Scholar 

  • Matsuki Y, Nakamura K, Watanabe H, Aoki T, Nakata H, Katsuragawa S, Doi K (2002) Usefulness of an artificial neural network for differentiating benign from malignant pulmonary nodules on high-resolution CT: evaluation with receiver operating characteristic analysis. AJR Am J Roentgenol 178:657–663

    Article  PubMed  Google Scholar 

  • Matsumoto S, Kundel HL, Gee JC, Gefter WB, Hatabu H (2006) Pulmonary nodule detection in CT images with quantized convergence index filter. Med Image Anal 10:343–352

    Article  PubMed  Google Scholar 

  • Mcnitt-Gray MF, Hart EM, Wyckoff N, Sayre JW, Goldin JG, Aberle DR (1999) A pattern classification approach to characterizing solitary pulmonary nodules imaged on high resolution CT: preliminary results. Med Phys 26:880–888

    Article  CAS  PubMed  Google Scholar 

  • Messay T, Hardie RC, Rogers SK (2010) A new computationally efficient CAD system for pulmonary nodule detection in CT imagery. Med Image Anal 14:390–406

    Article  PubMed  Google Scholar 

  • Metz CE (1986) ROC methodology in radiologic imaging. Investig Radiol 21:720–733

    Article  CAS  Google Scholar 

  • Meyers PH, Nice CM Jr., Becker H. C, Nettleton WJ, Jr., Sweeney JW, Meckstroth GR (1964) Automated computer analysis of radiographic images. Radiology 831029–1034.

    Google Scholar 

  • Miettinen OS, Henschke CI (2001) CT screening for lung cancer: coping with nihilistic recommendations. Radiology 221:592–596

    Article  CAS  PubMed  Google Scholar 

  • Mnih V, Kavukcuoglu K, Silver D, Rusu AA, Veness J, Bellemare MG, Graves A, Riedmiller M, Fidjeland AK, Ostrovski G, Petersen S, Beattie C, Sadik A, Antonoglou I, King H, Kumaran D, Wierstra D, Legg S, Hassabis D (2015) Human-level control through deep reinforcement learning. Nature 518:529–533

    Article  CAS  PubMed  Google Scholar 

  • Mori K, Niki N, Kondo T, Kamiyama Y, Kodama T, Kawada Y, Moriyama N (2005) Development of a novel computer-aided diagnosis system for automatic discrimination of malignant from benign solitary pulmonary nodules on thin-section dynamic computed tomography. J Comput Assist Tomogr 29:215–222

    Article  PubMed  Google Scholar 

  • Murphy K, Van Ginneken B, Schilham AM, De Hoop BJ, Gietema HA, Prokop M (2009) A large-scale evaluation of automatic pulmonary nodule detection in chest CT using local image features and k-nearest-neighbour classification. Med Image Anal 13:757–770

    Article  CAS  PubMed  Google Scholar 

  • Otsu N (1979) A threshold selection method from gray level histograms. IEEE Trans Syst Man Cyber 9:62–66

    Article  Google Scholar 

  • Pu J, Zheng B, Leader JK, Wang XH, Gur D (2008) An automated CT based lung nodule detection scheme using geometric analysis of signed distance field. Med Phys 35:3453–3461

    Article  PubMed  PubMed Central  Google Scholar 

  • Retico A, Delogu P, Fantacci ME, Gori I, Preite Martinez A (2008) Lung nodule detection in low-dose and thin-slice computed tomography. Comput Biol Med 38:525–534

    Article  CAS  PubMed  Google Scholar 

  • Riccardi A, Petkov TS, Ferri G, Masotti M, Campanini R (2011) Computer-aided detection of lung nodules via 3D fast radial transform, scale space representation, and Zernike MIP classification. Med Phys 38:1962–1971

    Article  PubMed  Google Scholar 

  • Roellinger FX Jr, Kahveci AE, Chang JK, Harlow CA, Dwyer SJ III, Lodwick GS (1973) Computer analysis of chest radiographs. Comp Graph Image Process 2:232–251

    Article  Google Scholar 

  • Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323:533–536

    Article  Google Scholar 

  • Sahiner B, Chan HP, Hadjiiski L (2008) Classifier performance prediction for computer-aided diagnosis using a limited dataset. Med Phys 35:1559–1570

    Article  PubMed  PubMed Central  Google Scholar 

  • Shah SK, Mcnitt-Gray MF, Rogers SR, Goldin JG, Suh RD, Sayre JW, Petkovska I, Kim HJ, Aberle DR (2005) Computer aided characterization of the solitary pulmonary nodule using volumetric and contrast enhancement features. Acad Radiol 12:1310–1319

    Article  PubMed  Google Scholar 

  • Sone S, Takashima S, Li F, Yang Z, Honda T, Maruyama Y, Hasegawa M, Yamanda T, Kubo K, Hanamura K, Asakura K (1998) Mass screening for lung cancer with mobile spiral computed tomography scanner. Lancet 351(9111):1242–1245

    Article  CAS  PubMed  Google Scholar 

  • Sone S, Li F, Yang ZG, Honda T, Maruyama Y, Takashima S, Hasegawa M, Kawakami S, Kubo K, Haniuda M, Yamanda T (2001) Results of three-year mass screening programme for lung cancer using mobile low-dose spiral computed tomography scanner. Br J Cancer 84(1):25–32

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Suzuki K (2004) Determining the receptive field of a neural filter. J Neural Eng 1:228–237

    Article  PubMed  Google Scholar 

  • Suzuki K (2009) A supervised ‘lesion-enhancement’ filter by use of a massive-training artificial neural network (MTANN) in computer-aided diagnosis (CAD). Phys Med Biol 54:S31–S45

    Article  PubMed  PubMed Central  Google Scholar 

  • Suzuki K (2012a) Pixel-based Machine Learning (PML) in medical imaging. Int J Biomed Imag 2012 Article ID 792079, 18 pages

    Google Scholar 

  • Suzuki K (2012b) A review of computer-aided diagnosis in thoracic and colonic imaging. Quant Imaging Med Surg 2:163–176

    PubMed  PubMed Central  Google Scholar 

  • Suzuki K (2013) Machine learning in computer-aided diagnosis of the thorax and colon in CT: a survey. IEICE Trans Inf Syst E96-D:772–783

    Article  PubMed  PubMed Central  Google Scholar 

  • Suzuki K, Doi K (2005) How can a massive training artificial neural network (MTANN) be trained with a small number of cases in the distinction between nodules and vessels in thoracic CT? Acad Radiol 12:1333–1341

    Article  PubMed  Google Scholar 

  • Suzuki K, Horiba I, Ikegaya K, Nanki M (1995) Recognition of coronary arterial stenosis using neural network on DSA system. Syst Comput Japan 26:66–74

    Article  Google Scholar 

  • Suzuki K, Horiba I, Sugie N (2001) A simple neural network pruning algorithm with application to filter synthesis. Neural Process Lett 13:43–53

    Article  Google Scholar 

  • Suzuki K, Horiba I, Sugie N (2002a) Efficient approximation of neural filters for removing quantum noise from images. IEEE Trans Signal Process 50:1787–1799

    Article  Google Scholar 

  • Suzuki K, Horiba I, Sugie N, Nanki M (2002b) Neural filter with selection of input features and its application to image quality improvement of medical image sequences. IEICE Trans Inf Syst E85-D:1710–1718

    Google Scholar 

  • Suzuki K, Armato SG 3rd, Li F, Sone S, Doi K (2003a) Massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose computed tomography. Med Phys 30:1602–1617

    Google Scholar 

  • Suzuki K, Horiba I, Sugie N (2003b) Linear-time connected-component labeling based on sequential local operations. Comput Vis Image Underst 89:1–23

    Article  Google Scholar 

  • Suzuki K, Horiba I, Sugie N (2003c) Neural edge enhancer for supervised edge enhancement from noisy images. IEEE Trans Pattern Anal Mach Intell 25:1582–1596

    Article  Google Scholar 

  • Suzuki, K., Abe, H., Li, F. & Doi, K. (2004a) Suppression of the contrast of ribs in chest radiographs by means of massive training artificial neural network. In: Proceedings of the SPIE Medical Imaging (SPIE MI), May San Diego, CA, pp 1109–1119

    Google Scholar 

  • Suzuki K, Horiba I, Sugie N, Nanki M (2004b) Extraction of left ventricular contours from left ventriculograms by means of a neural edge detector. IEEE Trans Med Imaging 23:330–339

    Article  PubMed  Google Scholar 

  • Suzuki K, Li F, Sone S, Doi K (2005a) 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 Trans Med Imaging 24:1138–1150

    Article  PubMed  Google Scholar 

  • Suzuki K, Shiraishi J, Abe H, MacMahon H, Doi K (2005b) False-positive reduction in computer-aided diagnostic scheme for detecting nodules in chest radiographs by means of massive training artificial neural network. Acad Radiol 12:191–201

    Article  PubMed  Google Scholar 

  • Suzuki K, Abe H, MacMahon H, Doi K (2006a) Image-processing technique for suppressing ribs in chest radiographs by means of massive training artificial neural network (MTANN). IEEE Trans Med Imaging 25:406–416

    Article  PubMed  Google Scholar 

  • Suzuki K, Yoshida H, Nappi J, Dachman AH (2006b) 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

    Article  PubMed  Google Scholar 

  • Suzuki K, Shi Z, Zhang J (2008a) Supervised enhancement of lesions by use of a massive-training artificial neural network (MTANN) in computer-aided diagnosis (CAD). Proc. Int. Conf. Pattern Recognition (ICPR), December 2008, Tampa, FL. MoCT6.3

    Google Scholar 

  • Suzuki K, Yoshida H, Nappi J, Armato SG 3rd, Dachman AH (2008b) Mixture of expert 3D massive-training ANNs for reduction of multiple types of false positives in CAD for detection of polyps in CT colonography. Med Phys 35:694–703

    Google Scholar 

  • Suzuki K, Rockey DC, Dachman AH (2010a) CT colonography: advanced computer-aided detection scheme utilizing MTANNs for detection of “missed” polyps in a multicenter clinical trial. Med Phys 37:12–21

    Article  PubMed  Google Scholar 

  • Suzuki K, Zhang J, Xu J (2010b) Massive-training artificial neural network coupled with Laplacian-eigenfunction-based dimensionality reduction for computer-aided detection of polyps in CT colonography. IEEE Trans Med Imaging 29:1907–1917

    Article  PubMed  PubMed Central  Google Scholar 

  • Swensen SJ, Jett JR, Hartman TE, Midthun DE, Sloan JA, Sykes AM, Aughenbaugh GL, Clemens MA (2003) Lung cancer screening with CT: Mayo Clinic experience. Radiology 226:756–761

    Article  PubMed  Google Scholar 

  • Tan M, Deklerck R, Jansen B, Bister M, Cornelis J (2011) A novel computer-aided lung nodule detection system for CT images. Med Phys 38:5630–5645

    Article  PubMed  Google Scholar 

  • Toriwaki J, Suenaga Y, Negoro T, Fukumura T (1973) Pattern recognition of chest x-ray images. Computer Graphics and Image Processing 2:252–271

    Article  Google Scholar 

  • Vapnik VN (1995) The Nature of Statistical Learning Theory. Springer, Berlin

    Book  Google Scholar 

  • Way TW, Sahiner B, Chan HP, Hadjiiski L, Cascade PN, Chughtai A, Bogot N, Kazerooni E (2009) Computer-aided diagnosis of pulmonary nodules on CT scans: improvement of classification performance with nodule surface features. Med Phys 36:3086–3098

    Article  PubMed  PubMed Central  Google Scholar 

  • Winsberg F, Elkin M, Macy J, Bordaz V, Weymouth W (1967) Detection of radiographic abnormalities in mammograms by means of optical scanning and computer analysis. Radiology 89:211–&

    Google Scholar 

  • Xu JW, Suzuki K (2014) Max-AUC feature selection in computer-aided detection of polyps in CT colonography. IEEE J Biomed Health Inform 18:585–593

    Article  PubMed  PubMed Central  Google Scholar 

  • Xu XW, Doi K, Kobayashi T, MacMahon H, Giger ML (1997) Development of an improved CAD scheme for automated detection of lung nodules in digital chest images. Med Phys 24:1395–1403

    Article  CAS  PubMed  Google Scholar 

  • Ye X, Lin X, Dehmeshki J, Slabaugh G, Beddoe G (2009) Shape-based computer-aided detection of lung nodules in thoracic CT images. IEEE Trans Biomed Eng 56:1810–1820

    Article  PubMed  Google Scholar 

  • Yuan R, Vos PM, Cooperberg PL (2006) Computer-aided detection in screening CT for pulmonary nodules. AJR Am J Roentgenol 186:1280–1287

    Article  PubMed  Google Scholar 

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Acknowledgments

The author is grateful to all members of the Suzuki laboratory, i.e., postdoctoral scholars, computer scientists, visiting scholars/professors, medical students, graduate/undergraduate students, research technicians, research volunteers, and support staff, in the Department of Radiology at the University of Chicago and in the Medical Imaging Research Center at the Illinois Institute of Technology, for their valuable suggestions and support. Computer-aided diagnosis and machine learning technologies developed at the University of Chicago have been licensed to companies including R2 Technology (Hologic), Riverain Medical (Riverain Technologies), AlgoMedica, Deus Technology, Median Technologies, Mitsubishi Space Software, General Electric, and Toshiba.

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  1. Medical Imaging Research Center & Department of Electrical and Computer Engineering, Illinois Institute of Technology, 3440 South Dearborn Street, Chicago, IL, 60616, USA

    Kenji Suzuki Ph.D.

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Correspondence to Kenji Suzuki Ph.D. .

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  1. Department of Health Sciences, Kyushu University, Fukuoka, Japan

    Hidetaka Arimura

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Suzuki, K. (2017). Computer-Aided Detection of Lung Cancer. In: Arimura, H. (eds) Image-Based Computer-Assisted Radiation Therapy. Springer, Singapore. https://doi.org/10.1007/978-981-10-2945-5_2

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