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Lauge Sørensen, S. Shaker, Marleen Bruijne (2010)
Quantitative Analysis of Pulmonary Emphysema Using Local Binary PatternsIEEE Transactions on Medical Imaging, 29
(2007)
the Coronary Angiography by Computed Tomography with the Use of a Submillimeter resolution (CACTUS) trial,” Eur
A. Leber, A. Becker, A. Knez, F. Ziegler, M. Sirol, K. Nikolaou, B. Ohnesorge, Z. Fayad, C. Becker, M. Reiser, G. Steinbeck, P. Boekstegers (2006)
Accuracy of 64-slice computed tomography to classify and quantify plaque volumes in the proximal coronary system: a comparative study using intravascular ultrasound.Journal of the American College of Cardiology, 47 3
(2009)
evaluation of nonculprit coronary lesions,” J
(2002)
clinical application of a computer-aided diagnosis system,” Eur
J. Hausleiter, Tanja Meyer, M. Hadamitzky, M. Zankl, P. Gerein, Katharina Dörrler, A. Kastrati, S. Martinoff, A. Schömig (2007)
Non-invasive coronary computed tomographic angiography for patients with suspected coronary artery disease: the Coronary Angiography by Computed Tomography with the Use of a Submillimeter resolution (CACTUS) trial.European heart journal, 28 24
M. Budoff, D. Dowe, J. Jollis, M. Gitter, J. Sutherland, Edward Halamert, M. Scherer, R. Bellinger, Arthur Martin, R. Benton, A. Delago, J. Min (2008)
Diagnostic performance of 64-multidetector row coronary computed tomographic angiography for evaluation of coronary artery stenosis in individuals without known coronary artery disease: results from the prospective multicenter ACCURACY (Assessment by Coronary Computed Tomographic Angiography of IndiJournal of the American College of Cardiology, 52 21
D. Wormanns, M. Fiebich, Mustafa Saidi, S. Diederich, W. Heindel (2002)
Automatic detection of pulmonary nodules at spiral CT: clinical application of a computer-aided diagnosis systemEuropean Radiology, 12
(2015)
Journal of Medical Imaging
H. Kirisli, M. Schaap, C. Metz, A. Dharampal, W. Meijboom, S. Papadopoulou, A. Dedic, K. Nieman, M. Graaf, M. Meijs, M. Cramer, A. Broersen, Suheyla Cetin, A. Eslami, L. Florez-Valencia, Kuo-Lung Lor, B. Matuszewski, I. Melki, Brian Mohr, Ilkay Öksüz, R. Shahzad, Chunliang Wang, P. Kitslaar, Gözde Ünal, A. Katouzian, M. Orkisz, Chung-Ming Chen, F. Precioso, Laurent Najman, S. Masood, D. Ünay, L. Vliet, R. Moreno, Roman Goldenberg, E. Vuçini, G. Krestin, W. Niessen, T. Walsum (2013)
Standardized evaluation framework for evaluating coronary artery stenosis detection, stenosis quantification and lumen segmentation algorithms in computed tomography angiographyMedical image analysis, 17 8
Structured learning algorithm for detection of nonobstructive and obstructive coronary
Toyohiko Tanaka, N. Nitta, Shinichi Ohta, Tsuyoshi Kobayashi, A. Kano, K. Tsuchiya, Yoko Murakami, Sawako Kitahara, M. Wakamiya, A. Furukawa, Masashi Takahashi, K. Murata (2009)
Evaluation of computer-aided detection of lesions in mammograms obtained with a digital phase-contrast mammography systemEuropean Radiology, 19
N. Chawla, N. Japkowicz, A. Kolcz (2004)
SPECIAL ISSUE ON LEARNING FROM IMBALANCED DATA SETS, 6
Biographies of the authors are not available
Dongwoo Kang, P. Slomka, R. Nakazato, V. Cheng, J. Min, Debiao Li, D. Berman, C.-C. Kuo, D. Dey (2012)
Automatic detection of significant and subtle arterial lesions from coronary CT angiography, 8314
R. Nishikawa (2007)
Current status and future directions of computer-aided diagnosis in mammographyComputerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society, 31 4-5
P. Sundaram, A. Zomorodian, C. Beaulieu, S. Napel (2008)
Colon polyp detection using smoothed shape operators: Preliminary resultsMedical image analysis, 12 2
Roman Goldenberg, D. Eilot, Grigory Begelman, E. Walach, Eyal Ben-Ishai, N. Peled (2012)
Computer-aided simple triage (CAST) for coronary CT angiography (CCTA)International Journal of Computer Assisted Radiology and Surgery, 7
B. Kelm, Sushil Mittal, Yefeng Zheng, A. Tsymbal, D. Bernhardt, F. Vega-Higuera, S. Zhou, P. Meer, D. Comaniciu (2011)
Detection, Grading and Classification of Coronary Stenoses in Computed Tomography AngiographyMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 14 Pt 3
Yefeng Zheng, Jianhua Shen, Hüseyin Tek, G. Funka-Lea (2012)
Model-Driven Centerline Extraction for Severely Occluded Major Coronary Arteries
R. Sadleir, P. Whelan (2002)
Colon centreline calculation for CT colonography using optimised 3D opological thinningProceedings. First International Symposium on 3D Data Processing Visualization and Transmission
(2008)
preliminary results,” Med
(2008)
results from the prospective multicenter ACCURACY (Assessment by Coronary Computed Tomographic Angiography of Individuals Undergoing Invasive Coronary Angiography) trial,” J
N. Chawla, N. Japkowicz, Aleksander Kotcz (2004)
Editorial: special issue on learning from imbalanced data setsSIGKDD Explor., 6
Ron Kohavi (1995)
A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection
(2006)
a comparative study using intravascular ultrasound,” J
E. Halpern, David Halpern (2011)
Diagnosis of coronary stenosis with CT angiography comparison of automated computer diagnosis with expert readings.Academic radiology, 18 3
S. Achenbach, F. Moselewski, D. Ropers, M. Ferencik, U. Hoffmann, B. Macneill, K. Pohle, U. Baum, K. Anders, I. Jang, W. Daniel, T. Brady (2003)
Detection of Calcified and Noncalcified Coronary Atherosclerotic Plaque by Contrast-Enhanced, Submillimeter Multidetector Spiral Computed Tomography: A Segment-Based Comparison With Intravascular UltrasoundCirculation: Journal of the American Heart Association, 109
G. Brunner, D. Chittajallu, U. Kurkure, I. Kakadiaris (2010)
Toward the automatic detection of coronary artery calcification in non-contrast computed tomography dataThe International Journal of Cardiovascular Imaging, 26
S. Yusuf, S. Reddy, S. Ôunpuu, Sonia Anand (2001)
Global burden of cardiovascular diseases: part I: general considerations, the epidemiologic transition, risk factors, and impact of urbanization.Circulation, 104 22
I. Išgum, A. Rutten, M. Prokop, B. Ginneken (2007)
Detection of coronary calcifications from computed tomography scans for automated risk assessment of coronary artery disease.Medical physics, 34 4
T. Freer, M. Ulissey (2001)
Screening mammography with computer-aided detection: prospective study of 12,860 patients in a community breast center.Radiology, 220 3
(2002)
a systematic study,” Intell
M. Graaf, A. Broersen, P. Kitslaar, C. Roos, J. Dijkstra, B. Lelieveldt, J. Jukema, M. Schalij, V. Delgado, Jeroen Bax, J. Reiber, A. Scholte (2013)
Automatic quantification and characterization of coronary atherosclerosis with computed tomography coronary angiography: cross-correlation with intravascular ultrasound virtual histologyThe International Journal of Cardiovascular Imaging, 29
S. Nawano, Koji Murakami, Noriyuki Moriyama, H. Kobatake, Hideya Takeo, Kazuo Shimura (1999)
Computer-aided diagnosis in full digital mammography.Investigative radiology, 34 4
M. Dinesh, P. Devarakota, J. Kumar (2010)
Automatic detection of plaques with severe stenosis in coronary vessels of CT angiography, 7624
E. DeLong, D. DeLong, D. Clarke‐Pearson (1988)
Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach.Biometrics, 44 3
(2010)
Perioperative β-Blockers : Use With Caution Perioperative β Blockers in Patients Having Non-Cardiac Surgery : A Meta-Analysis
F. Pugliese, M. Hunink, K. Gruszczyńska, F. Alberghina, R. Malagò, N. Pelt, N. Mollet, Filippo Cademartiri, A. Weustink, W. Meijboom, C. Witteman, P. Feyter, G. Krestin (2009)
Learning curve for coronary CT angiography: what constitutes sufficient training?Radiology, 251 2
Salih Göktürk, Carlo Tomasi, B. Acar, C. Beaulieu, D. Paik, R. Jeffrey, J. Yee, S. Napel (2001)
A statistical 3-D pattern processing method for computer-aided detection of polyps in CT colonographyIEEE Transactions on Medical Imaging, 20
H. Drucker, C. Burges, L. Kaufman, Alex Smola, V. Vapnik (1996)
Support Vector Regression Machines
(2012)
Coronary artery stenoses detection with random forest
S. Achenbach, U. Ropers, A. Kuettner, K. Anders, T. Pflederer, S. Komatsu, W. Bautz, W. Daniel, D. Ropers (2008)
Randomized comparison of 64-slice single- and dual-source computed tomography coronary angiography for the detection of coronary artery disease.JACC. Cardiovascular imaging, 1 2
(2011)
a multidetector computed tomography study,” J
Dongwoo Kang, P. Slomka, R. Nakazato, R. Arsanjani, V. Cheng, J. Min, Debiao Li, D. Berman, C.-C. Kuo, D. Dey (2013)
Automated knowledge-based detection of nonobstructive and obstructive arterial lesions from coronary CT angiography.Medical physics, 40 4
Corinna Cortes, V. Vapnik (1995)
Support-Vector NetworksMachine Learning, 20
K. Kofoed, T. Kristensen, Tobias Kühl, W. Nielsen, M. Nielsen, H. Kelbæk (2010)
PROGNOSTIC IMPLICATIONS OF NON-OBSTRUCTIVE CORONARY PLAQUES IN PATIENTS WITH NON-ST-SEGMENT ELEVATION MYOCARDIAL INFARCTION - A MULTIDETECTOR COMPUTED TOMOGRAPHY STUDYJournal of the American College of Cardiology, 55
D. Dey, V. Cheng, P. Slomka, R. Nakazato, A. Ramesh, S. Gurudevan, G. Germano, D. Berman (2009)
Automated 3-dimensional quantification of noncalcified and calcified coronary plaque from coronary CT angiography.Journal of cardiovascular computed tomography, 3 6
R. Uppaluri, E. Hoffman, M. Sonka, Patrick Hartley, G. Hunninghake, G. McLennan (1999)
Computer recognition of regional lung disease patterns.American journal of respiratory and critical care medicine, 160 2
W. Meijboom, C. Mieghem, N. Mollet, F. Pugliese, A. Weustink, N. Pelt, Filippo Cademartiri, K. Nieman, E. Boersma, P. Jaegere, G. Krestin, P. Feyter (2007)
64-slice computed tomography coronary angiography in patients with high, intermediate, or low pretest probability of significant coronary artery disease.Journal of the American College of Cardiology, 50 15
Yan Sun, L. Bielak, P. Peyser, S. Turner, P. Sheedy, E. Boerwinkle, S. Kardia (2008)
Application of machine learning algorithms to predict coronary artery calcification with a sibship‐based designGenetic Epidemiology, 32
(2010)
initial experience,” Eur
X. Guo, Yilong Yin, Cailing Dong, Gongping Yang, Guang-Tong Zhou (2008)
On the Class Imbalance Problem2008 Fourth International Conference on Natural Computation, 4
T. Stavngaard, S. Shaker, K. Bach, B. Stoel, A. Dirksen (2006)
Quantitative assessment of regional emphysema distribution in patients with chronic obstructive pulmonary disease (COPD)Acta Radiologica, 47
J. Dodge, B. Brown, E. Bolson, H. Dodge, Greg Brown (1988)
Intrathoracic spatial location of specified coronary segments on the normal human heart. Applications in quantitative arteriography, assessment of regional risk and contraction, and anatomic display.Circulation, 78 5 Pt 1
G. Stone, A. Maehara, A. Lansky, B. Bruyne, Ecaterina Cristea, G. Mintz, R. Mehran, J. Mcpherson, Naim Farhat, S. Marso, H. Parise, B. Templin, Roseann White, Zhen Zhang, P. Serruys (2011)
A prospective natural-history study of coronary atherosclerosis.The New England journal of medicine, 364 3
E. Arnoldi, M. Gebregziabher, U. Schoepf, Roman Goldenberg, L. Ramos-Duran, P. Zwerner, K. Nikolaou, M. Reiser, P. Costello, C. Thilo (2009)
Abstract 211: Automated Computer Aided Stenosis Detection at Coronary CT Angiography -Initial ExperienceCirculation, 120
I. Sluimer, P. Waes, M. Viergever, B. Ginneken (2003)
Computer-aided diagnosis in high resolution CT of the lungs.Medical physics, 30 12
N. Japkowicz, Shaju Stephen (2002)
The class imbalance problem: A systematic studyIntell. Data Anal., 6
D. Dey, T. Schepis, M. Marwan, P. Slomka, D. Berman, S. Achenbach (2010)
Automated three-dimensional quantification of noncalcified coronary plaque from coronary CT angiography: comparison with intravascular US.Radiology, 257 2
M. Petranović, Anand Soni, Hiram Bezzera, R. Loureiro, A. Sarwar, C. Raffel, E. Pomerantsev, I. Jang, T. Brady, S. Achenbach, R. Cury (2009)
Assessment of nonstenotic coronary lesions by 64-slice multidetector computed tomography in comparison to intravascular ultrasound: evaluation of nonculprit coronary lesions.Journal of cardiovascular computed tomography, 3 1
M. Boogers, A. Broersen, J. Velzen, F. Graaf, H. El-Naggar, P. Kitslaar, J. Dijkstra, V. Delgado, E. Boersma, A. Roos, J. Schuijf, M. Schalij, J. Reiber, Jeroen Bax, J. Jukema (2012)
Automated quantification of coronary plaque with computed tomography: comparison with intravascular ultrasound using a dedicated registration algorithm for fusion-based quantification.European heart journal, 33 8
G. Raff, A. Abidov, S. Achenbach, D. Berman, L. Boxt, M. Budoff, V. Cheng, T. Defrance, J. Hellinger, R. Karlsberg (2009)
SCCT guidelines for the interpretation and reporting of coronary computed tomographic angiography.Journal of cardiovascular computed tomography, 3 2
G. Kiss, J. Cleynenbreugel, M. Thomeer, P. Suetens, G. Marchal (2002)
Computer-aided diagnosis in virtual colonography via combination of surface normal and sphere fitting methodsEuropean Radiology, 12
Kenji Suzuki, I. Horiba, K. Ikegaya, M. Nanki (1995)
Recognition of Coronary Arterial Stenosis Using Neural Network on DSA SystemSystems and Computers in Japan, 26
Sushil Mittal, Yefeng Zheng, B. Georgescu, Fernando Higuera, S. Zhou, P. Meer, D. Comaniciu (2010)
Fast Automatic Detection of Calcified Coronary Lesions in 3D Cardiac CT Images
T. Ho, J. Hull, S. Srihari (1994)
Decision Combination in Multiple Classifier SystemsIEEE Trans. Pattern Anal. Mach. Intell., 16
Abstract. Visual identification of coronary arterial lesion from three-dimensional coronary computed tomography angiography (CTA) remains challenging. We aimed to develop a robust automated algorithm for computer detection of coronary artery lesions by machine learning techniques. A structured learning technique is proposed to detect all coronary arterial lesions with stenosis ≥ 25 % . Our algorithm consists of two stages: (1) two independent base decisions indicating the existence of lesions in each arterial segment and (b) the final decision made by combining the base decisions. One of the base decisions is the support vector machine (SVM) based learning algorithm, which divides each artery into small volume patches and integrates several quantitative geometric and shape features for arterial lesions in each small volume patch by SVM algorithm. The other base decision is the formula-based analytic method. The final decision in the first stage applies SVM-based decision fusion to combine the two base decisions in the second stage. The proposed algorithm was applied to 42 CTA patient datasets, acquired with dual-source CT, where 21 datasets had 45 lesions with stenosis ≥ 25 % . Visual identification of lesions with stenosis ≥ 25 % by three expert readers, using consensus reading, was considered as a reference standard. Our method performed with high sensitivity (93%), specificity (95%), and accuracy (94%), with receiver operator characteristic area under the curve of 0.94. The proposed algorithm shows promising results in the automated detection of obstructive and nonobstructive lesions from CTA.
Journal of Medical Imaging – SPIE
Published: Jan 1, 2015
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