Abstract
Purpose
For accurate evaluation of myocardial perfusion on computed tomography images, precise identification of the myocardial borders of the left ventricle (LV) is mandatory. In this article, we propose a method to detect the contour of LV myocardium automatically and accurately.
Methods
Our detection method is based on active shape model. For precise detection, we estimate the pose and shape parameters separately by three steps: LV coordinate system estimation, myocardial shape estimation, and transformation. In LV coordinate system estimation, we detect heart features followed by the entire LV by introducing machine-learning approach. Since the combination of two types feature detection covers the LV variation, such as pose or shape, we can estimate the LV coordinate system robustly. In myocardial shape estimation, we minimize the energy function including pattern error around myocardium with adjustment of pattern model to input image using estimated concentration of contrast dye. Finally, we detect LV myocardial contours in the input images by transforming the estimated myocardial shape using the matrix composed of the vectors calculated by the LV coordinate system estimation.
Results
In our experiments with 211 images from 145 patients, mean myocardial contours point-to-point errors for our method as compared to ground truth were 1.02 mm for LV endocardium and 1.07 mm for LV epicardium. The average computation time was 2.4 s (on a 3.46 GHz processor with 2-multithreading process).
Conclusions
Our method achieved accurate and fast myocardial contour detection which may be sufficient for myocardial perfusion examination.
Similar content being viewed by others
References
Causes of death in 2008 (2008) Global health observatory. World Health Organization. http://www.who.int/gho/mortality_burden_disease/causes_death_2008/en/index.html. Accessed 12 Dec 2011
George RT, Silva C, Cordeiro MAS, DiPaula A, Thompson DR, McCarthy WF, Ichihara T, Lima JAC, Lardo AC (2006) Multidetector computed tomography myocardial perfusion imaging during adenosine stress. J Am Coll Cardiol 48(1): 153–160
George RT, Zadeh AA, Miller JM, Kitagawa K, Chang HJ, Bluemke DA, Becker L, Yousuf O, Texter J, Lardo AC, Lima JAC (2009) Adenosine stress 64- and 256-row detector computed tomography angiography and perfusion imaging: a pilot study evaluating the transmural extent of perfusion to predict atherosclerosis causing myocardial ischemia. Circ Cardiovasc Imaging 2: 174–182
George RT, Ichihara T, Lima JAC, Lardo AC (2010) A method for reconstructing the arterial input function during helical CT: implications for myocardial perfusion distribution imaging. Radiology 255(2): 396–404
Choi S, Kim H, Oh J, Kang T, Sun K, Kim M (2007) Segmentation of the left ventricle in myocardial perfusion SPECT using variational level set formulation. In: IEEE nuclear science symposium conference record M13-253, pp 3060–3064
Tobon-Gomez C, Butakoff C, Aguade S, Sukno F, Moragas G, Frangi AF (2008) Automatic construction of 3D-ASM intensity models by simulating image acquisition: application to myocardial gated SPECT studies. IEEE Trans Med Imaging 27(11): 1655–1667
Berbari RE, Bloch I, Redheuil A, Angelini E, Mousseaux E, Frouin F, Herment A (2007) An automated myocardial segmentation in cardiac MRI. In: Proceedings of the 29th annual international conference of the IEEE EMBS, pp 4508–4511
Liu L, Wu Y, Wang Y (2009) A novel method for segmentation of the cardiac MR images using generalized DDGVF snake models with shape priors. Inf Technol J 8(4): 486–494
Lee HY, Codella NCF, Cham MD, Weinsaft JW, Wang Y (2010) Automatic left ventricle segmentation using iterative thresholding and an active contour model with adaptation on short-axis cardiac MRI. IEE Trans Med Imaging 57(4): 905–913
Horkaew P (2010) Analysis of CMR perfusion imaging based on statistical appearance models. In: Proceedings of the second international conference on knowledge and smart technologies, pp 6–11
Assen HC, Danilouchkine MG, Frangi AF, Ordás S, Westenberg JJM, Reiber JHC, Lelieveldt BPF (2006) SPASM: a 3D-ASM for segmentation of sparse and arbitrarily oriented cardiac MRI data. Med Image Anal 10: 286–303
Neubauer A, Wegenkittl R (2002) Analysis of four-dimensional cardiac data sets using skeleton-based segmentation. In: Proceedings of winter school of computer graphics, pp 330–337
Fritz D, Rinck D, Unterhinninghofen R, Dillmann R, Scheuering M (2005) Automatic segmentation of the left ventricle and computation of diagnostic parameters using regiongrowing and a statistical model. In: Proceedings of SPIE, vol 5747, pp 1844–1854
Jolly MP (2006) Automatic segmentation of the left ventricle in cardiac MR and CT images. Int J Comput Vis 70(2): 151–163
Ecabert O, Peters J, Schramm H, Lorenz C, Berg J, Walker MJ, Vembar M, Olszewski ME, Subramanyan K, Lavi G, Weese J (2008) Automatic model-based segmentation of the heart in CT images. IEEE Trans Med Imaging 27(9): 1189–1201
Zheng Y, Barbu A, Georgescu B, Scheuering M, Comaniciu D (2008) Four-chamber heart modeling and automatic segmentation for 3-D cardiac CT volumes using marginal space learning and steerable features. IEEE Trans Med Imaging 27(11): 1668–1681
Cootes TF, Hill A, Taylor CJ, Haslam J (1994) The use of active shape models for locating structures in medical images. Image Vis Comput 12(6): 355–366
Cootes TF, Taylor CJ, Cooper DH, Graham J (1995) Active shape models-their training and application. Comput Vis Image Underst 61(1): 38–59
Kirisli HA, Schaap M, Klein S, Neefjes LA, Weustink AC, Walsum T, Niessen WJ (2010) Fully automatic cardiac segmentation from 3D CTA data: a multi-atlas based approach. In: Proceedings of SPIE, vol 7623, pp 762305-1–762305-9
Frangi AF (2001) Three-dimensional modeling for functional analysis of cardiac images: a review. IEEE Trans Med Imaging 20(1): 2–25
Geurts P, Ernst D, Wehenkel L (2006) Extremely randomized trees. Mach Learn 36(1): 3–42
Nowak E, Jurie F (2007) Learning visual similarity measures for comparing never seen objects. Comput Vis Pattern Recognit, pp 1–8
Fukui K (1995) Edge extraction method based on separability of image features. IEICE Trans Inf Syst E78-D(12): 1533–1538
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Sugiura, T., Takeguchi, T., Sakata, Y. et al. Automatic model-based contour detection of left ventricle myocardium from cardiac CT images. Int J CARS 8, 145–155 (2013). https://doi.org/10.1007/s11548-012-0692-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11548-012-0692-7