Abstract
This paper presents a machine-learning algorithm for the automatic localization of myocardial infarct in the left ventricle. Our method constructs neighbourhood approximation forests, which are trained with previously diagnosed 4D cardiac sequences. We introduce a new set of features that simultaneously exploit information from the shape and motion of the myocardial wall along the cardiac cycle. More precisely, characteristics are extracted from a hyper surface that represents the profile of the myocardial thickness. The method has been tested on a database of 65 cardiac MRI images in order to retrieve the diagnosed infarct area. The results demonstrate the effectiveness of the NAF in predicting the left ventricular infarct location in 7 distinct regions. We evaluated our method by verifying the database ground truth. Following a new examination of the 4D cardiac images, our algorithm may detect misclassified infarct locations in the database.
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Fonseca, C., Backhaus, M., Bluemke, D., Britten, R., Chung, J., Cowan, B., Dinov, I., Finn, J., Hunter, P., Kadish, A., Lee, D., Lima, J., Medrano-Gracia, P., Shivkumar, K., Suinesiaputra, A., Tao, W., Young, A.: The cardiac atlas project. An imaging database for computational modeling and statistical atlases of the heart. Bioinformatics 27(16), 2288–2295 (2011)
Perperidis, D., Mohiaddin, R.H., Rueckert, D.: Construction of a 4D statistical atlas of the cardiac anatomy and its use in classification. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3750, pp. 402–410. Springer, Heidelberg (2005)
Medrano-Gracia, P., Suinesiaputra, A., Cowan, B., Bluemke, D., Frangi, A., Lee, D., Lima, J., Young, A.: An atlas for cardiac MRI regional wall motion and infarct scoring. In: Camara, O., Mansi, T., Pop, M., Rhode, K., Sermesant, M., Young, A. (eds.) STACOM 2012. LNCS, vol. 7746, pp. 188–197. Springer, Heidelberg (2013)
Wei, D., Sun, Y., Ong, S., Chai, P., Teo, L., Low, A.: Three-dimensional segmentation of the left ventricle in late gadolinium enhanced MR images of chronic infarction combining long-and short-axis information. Med. Image Anal. 17(6), 685–697 (2013)
Rohlfing, T., Brandt, R., Menzel, R., Russakoff, D.B., Maurer Jr., C.R.: Quo vadis, atlas-based segmentation. In: Handbook of Biomedical Image Analysis, pp. 435–486. Springer US, New York (2005)
Heckemann, R., Keihaninejad, S., Aljabar, P., Rueckert, D., Hajnal, J., Hammers, A.: Improving intersubject image registration using tissue-class information benefits robustness and accuracy of multi-atlas based anatomical segmentation. Neuroimage 51(1), 221–227 (2010)
Müller, H., Michoux, N., Bandon, D., Geissbuhler, A.: A review of content-based image retrieval systems in medical applications - clinical benefits and future directions. Int. J. Med. Inform. 73(1), 1–23 (2004)
André, B., Vercauteren, T., Buchner, A., Wallace, M., Ayache, N.: A smart atlas for endomicroscopy using automated video retrieval. Med. Image Anal. 15(4), 460–476 (2011)
Margeta, J., Geremia, E., Criminisi, A., Ayache, N.: Layered spatio-temporal forests for left ventricle segmentation from 4D cardiac MRI data. In: Camara, O., Konukoglu, E., Pop, M., Rhode, K., Sermesant, M., Young, A. (eds.) STACOM 2011. LNCS, vol. 7085, pp. 109–119. Springer, Heidelberg (2012)
Swets, D., Weng, J.: Using discriminant eigenfeatures for image retrieval. IEEE T. Pattern Anal. 8, 831–836 (1996)
Konukoglu, E., Glocker, B., Zikic, D., Criminisi, A.: Neighbourhood approximation using randomized forests. Med. Image Anal. 17(7), 790–804 (2013)
Cerqueira, M., Weissman, N., Dilsizian, V., Jacobs, A., Kaul, S., Laskey, W., Pennell, D., Rumberger, J., Ryan, T., Verani, M.: Standardized myocardial segmentation and nomenclature for tomographic imaging of the heart. Circulation 105, 539–542 (2002)
Acknowledgements
The authors wish to thank Alistair Young for providing the DETERMINE database. This research is partially funded by the ERC Advanced Grant MedYMAFunding.
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Bleton, H., Margeta, J., Lombaert, H., Delingette, H., Ayache, N. (2016). Myocardial Infarct Localization Using Neighbourhood Approximation Forests. In: Camara, O., Mansi, T., Pop, M., Rhode, K., Sermesant, M., Young, A. (eds) Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges. STACOM 2015. Lecture Notes in Computer Science(), vol 9534. Springer, Cham. https://doi.org/10.1007/978-3-319-28712-6_12
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DOI: https://doi.org/10.1007/978-3-319-28712-6_12
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