Guided review by frequent itemset mining: additional evidence for plaque detection
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A guided review process to support manual coronary plaque detection in computed tomography coronary angiography (CTCA) data sets is proposed. The method learns the spatial plaque distribution patterns by using the frequent itemset mining algorithm and uses this knowledge to predict potentially missed plaques during detection.
Materials and methods
Plaque distribution patterns from 252 manually labeled patients who underwent CTCA were included. For various cross-validations a labeling with missing plaques was created from the initial manual ground truth labeling. Frequent itemset mining was used to learn the spatial plaque distribution patterns in form of association rules from a training set. These rules were then applied on a testing set to search for segments in the coronary tree showing evidence of containing unlabeled plaques. The segments with potentially missed plaques were finally reviewed for the existence of plaques. The proposed guided review was compared to a weighted random approach that considered only the probability of occurrence for a plaque in a specific segment and not its spatial correlation to other plaques.
Guided review by frequent itemset mining performed significantly better (p < 0.001) than the reference weighted random approach in predicting coronary segments with initially missed plaques. Up to 47% of the initially removed plaques were refound by only reviewing 4.4% of all possible segments.
The spatial distribution patterns of atherosclerosis in coronary arteries can be used to predict potentially missed plaques by a guided review with frequent itemset mining. It shows potential to reduce the intra- and inter-observer variability.
KeywordsComputed tomography Plaque Plaque distribution patterns Spatial distribution Data mining
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- 1.Jougla E (2003) Health Statistics—Atlas on mortality in the European Union. European CommunitiesGoogle Scholar
- 6.Achenbach S, Moselewski F, Ropers D et al (2004) Detection of calcified and noncalcified coronary atherosclerotic plaque by contrast-enhanced, submillimeter multidetector spiral computed tomography: a segment-based comparison with intravascular ultrasound. Circulation 109(1): 14–17. doi:10.1161/01.CIR.0000111517.69230.0F PubMedCrossRefGoogle Scholar
- 8.Hoffmann U, Moselewski F, Nieman K et al (2006) Noninvasive assessment of plaque morphology and composition in culprit and stable lesions in acute coronary syndrome and stable lesions in stable angina by multidetector computed tomography. J Am Coll Cardiol 47(8): 1655–1662. doi:10.1016/j.jacc.2006.01.041 PubMedCrossRefGoogle Scholar
- 10.Leber AW, Knez A, von Ziegler F et al (2005) Quantification of obstructive and nonobstructive coronary lesions by 64-slice computed tomography: a comparative study with quantitative coronary angiography and intravascular ultrasound. J Am Coll Cardiol 46(1): 147–154. doi:10.1016/j.jacc.2005.03.071 PubMedCrossRefGoogle Scholar
- 11.Saur SC, Alkadhi H, Desbiolles L et al (2008) Automatic detection of calcified coronary plaques in computed tomography data sets. In: Medical image computing and computer-assisted intervention—MICCAI 2008. Springer, New York. doi:10.1007/978-3-540-85988-8_21
- 12.Agrawal R, Imieliński T, Swami A (1993) Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACM SIGMOD international conference on Management of data. Washington, D.C., pp 207–216Google Scholar
- 13.Quack T, Ferrari V, Leibe B et al (2007) Efficient mining of frequent and distinctive feature configurations. In: International conference on computer vision. Rio de Janeiro, Brasil. doi:10.1109/ICCV.2007.4408906
- 16.Wright A, Sittig DF (2006) Automated development of order sets and corollary orders by data mining in an ambulatory computerized physician order entry system. In: AMIA annual symposium proceedingsGoogle Scholar
- 17.Couturier O, Delalin H, Fu H et al (2004) A three-step approach for STULONG database analysis: characterization of patients’ groups. In: ECML-PKDD discovery challengeGoogle Scholar
- 23.Shimada Y, Courtney BK, Nakamura M et al (2006) Intravascular ultrasonic analysis of atherosclerotic vessel remodeling and plaque distribution of stenotic left anterior descending coronary arterial bifurcation lesions upstream and downstream of the side branch. Am J Cardiol 98(2): 193–196. doi:10.1016/j.amjcard.2006.01.073 PubMedCrossRefGoogle Scholar
- 28.Austen W, Edwards J, Frye R et al (1975) A reporting system on patients evaluated for coronary artery disease. Report of the ad hoc committee for grading of coronary artery disease, council on cardiovascular surgery, American Heart Association. Circulation (51):5–40Google Scholar
- 29.Borgelt C, Kruse R (2002) Induction of association rules: a priori implementation. In: Conference on Computational StatisticsGoogle Scholar
- 30.Kerwin W, Han C, Chu B et al (2001) A quantitative vascular analysis system for evaluation of atherosclerotic lesions by MRI. In: Medical image computing and computer-assisted intervention—MICCAI. pp 786–794Google Scholar
- 31.Frangi AF, Niessen WJ, Nederkoorn PJ et al (2000) Three-dimensional model-based stenosis quantification of the carotid arteries from contrast-enhanced MR angiography. In: Proceedings of IEEE workshop on mathematical methods in biomedical image analysis. doi:10.1109/MMBIA.2000.852367