Development and Clinical Validation of a Hybrid Method for Semiautomated Left Ventricle Endocardial and Epicardial Boundary Extraction on Cine-magnetic Resonance Images

Overview

Journal J Med Imaging (Bellingham)

Specialty Radiology

Date 2018 Apr 18

PMID 29662919

Authors

Mahammed Messadi

Abdelhafid Bessaid

Denis Mariano-Goulart

Faycal Ben Bouallegue

Affiliations

Soon will be listed here.

Abstract

We describe a hybrid method for left ventricle (LV) endocardial and epicardial segmentation on cardiac magnetic resonance (CMR) images requiring minimal operator intervention. Endocardium extraction results from the union of three independent estimations based on adaptive thresholding, region growing, and active contour with Chan-Vese energy function. Epicardium segmentation relies on conditional morphological dilation of the endocardial mask followed by active contour optimization. The proposed method was first evaluated using an open access database of 18 CMR for which expert manual contouring was available. The method was further validated on a retrospective cohort of 29 patients, who underwent CMR with expert manual segmentation. Regarding the open access database, similarity (Dice index) between hybrid and expert segmentations was good for end-diastolic (ED) endocardium (0.92), end-systolic (ES) endocardium (0.88), and ED epicardium (0.92). As for derived LV parameters, concordance (Lin's coefficient) was good for ED volume (0.91), ES volume (0.93), ejection fraction (EF; 0.89), and fair for myocardial mass (MM; 0.74). Regarding the retrospective patient study, concordance between expert and hybrid estimations was excellent for ED volume (0.95), ES volume (0.96), good for EF (0.86), and fair for MM (0.71). Hybrid segmentation resulted in small biases ([Formula: see text] for ED volume, [Formula: see text] for ES volume, [Formula: see text] for EF, and [Formula: see text] for MM) with little clinical relevance and acceptable for routine practice. The quickness and robustness of the proposed hybrid method and its ability to provide LV volumes, functions, and masses highly concordant with those given by expert segmentation support its pertinence for routine clinical use.

References

Sibille L, Ben Bouallegue F, Bourdon A, Micheau A, Vernhet-Kovacsik H, Mariano-Goulart D . Comparative values of gated blood-pool SPECT and CMR for ejection fraction and volume estimation. Nucl Med Commun. 2010; 32(2):121-8. DOI: 10.1097/MNM.0b013e32834155f1. View

Sievers B, Kirchberg S, Bakan A, Franken U, Trappe H . Impact of papillary muscles in ventricular volume and ejection fraction assessment by cardiovascular magnetic resonance. J Cardiovasc Magn Reson. 2004; 6(1):9-16. DOI: 10.1081/jcmr-120027800. View

Ma Y, Wang L, Ma Y, Dong M, Du S, Sun X . An SPCNN-GVF-based approach for the automatic segmentation of left ventricle in cardiac cine MR images. Int J Comput Assist Radiol Surg. 2016; 11(11):1951-1964. DOI: 10.1007/s11548-016-1429-9. View

Suinesiaputra A, Bluemke D, Cowan B, Friedrich M, Kramer C, Kwong R . Quantification of LV function and mass by cardiovascular magnetic resonance: multi-center variability and consensus contours. J Cardiovasc Magn Reson. 2015; 17:63. PMC: 4517503. DOI: 10.1186/s12968-015-0170-9. View

Petitjean C, Dacher J . A review of segmentation methods in short axis cardiac MR images. Med Image Anal. 2011; 15(2):169-84. DOI: 10.1016/j.media.2010.12.004. View

Lee H, Codella N, Cham M, Weinsaft J, Wang Y . Automatic left ventricle segmentation using iterative thresholding and an active contour model with adaptation on short-axis cardiac MRI. IEEE Trans Biomed Eng. 2009; 57(4):905-13. DOI: 10.1109/TBME.2009.2014545. View

Balzer P, Furber A, Croue A, TADEI A, Geslin P, Jallet P . Simultaneous and correlated detection of endocardial and epicardial borders on short-axis MR images for the measurement of left ventricular mass. Radiographics. 1998; 18(4):1009-18. DOI: 10.1148/radiographics.18.4.9672983. View

Zhang H, Wahle A, Johnson R, Scholz T, Sonka M . 4-D cardiac MR image analysis: left and right ventricular morphology and function. IEEE Trans Med Imaging. 2009; 29(2):350-64. PMC: 2849009. DOI: 10.1109/TMI.2009.2030799. View

Peng P, Lekadir K, Gooya A, Shao L, Petersen S, Frangi A . A review of heart chamber segmentation for structural and functional analysis using cardiac magnetic resonance imaging. MAGMA. 2016; 29(2):155-95. PMC: 4830888. DOI: 10.1007/s10334-015-0521-4. View

10.

Mitchell S, Lelieveldt B, van der Geest R, Bosch H, Reiber J, Sonka M . Multistage hybrid active appearance model matching: segmentation of left and right ventricles in cardiac MR images. IEEE Trans Med Imaging. 2001; 20(5):415-23. DOI: 10.1109/42.925294. View

11.

Paragios N . A level set approach for shape-driven segmentation and tracking of the left ventricle. IEEE Trans Med Imaging. 2003; 22(6):773-6. DOI: 10.1109/TMI.2003.814785. View

12.

Chan T, Vese L . Active contours without edges. IEEE Trans Image Process. 2008; 10(2):266-77. DOI: 10.1109/83.902291. View

13.

Lorenzo-Valdes M, Sanchez-Ortiz G, Elkington A, Mohiaddin R, Rueckert D . Segmentation of 4D cardiac MR images using a probabilistic atlas and the EM algorithm. Med Image Anal. 2004; 8(3):255-65. DOI: 10.1016/j.media.2004.06.005. View

14.

Thiele H, Paetsch I, Schnackenburg B, Bornstedt A, Grebe O, Wellnhofer E . Improved accuracy of quantitative assessment of left ventricular volume and ejection fraction by geometric models with steady-state free precession. J Cardiovasc Magn Reson. 2002; 4(3):327-39. DOI: 10.1081/jcmr-120013298. View

15.

Ben Ayed I, Li S, Ross I . Embedding overlap priors in variational left ventricle tracking. IEEE Trans Med Imaging. 2009; 28(12):1902-13. DOI: 10.1109/TMI.2009.2022087. View

16.

Lynch M, Ghita O, Whelan P . Left-ventricle myocardium segmentation using a coupled level-set with a priori knowledge. Comput Med Imaging Graph. 2006; 30(4):255-62. DOI: 10.1016/j.compmedimag.2006.03.009. View

17.

Lynch M, Ghita O, Whelan P . Automatic segmentation of the left ventricle cavity and myocardium in MRI data. Comput Biol Med. 2005; 36(4):389-407. DOI: 10.1016/j.compbiomed.2005.01.005. View

18.

Lotjonen J, Kivisto S, Koikkalainen J, Smutek D, Lauerma K . Statistical shape model of atria, ventricles and epicardium from short- and long-axis MR images. Med Image Anal. 2004; 8(3):371-86. DOI: 10.1016/j.media.2004.06.013. View

19.

Nachtomy E, Cooperstein R, Vaturi M, Bosak E, Vered Z, Akselrod S . Automatic assessment of cardiac function from short-axis MRI: procedure and clinical evaluation. Magn Reson Imaging. 1998; 16(4):365-76. DOI: 10.1016/s0730-725x(98)80019-2. View

20.

Pednekar A, Kurkure U, Muthupillai R, Flamm S, Kakadiaris I . Automated left ventricular segmentation in cardiac MRI. IEEE Trans Biomed Eng. 2006; 53(7):1425-8. DOI: 10.1109/TBME.2006.873684. View