Abstract
The left ventricle dysfunction is the root cause for major cardiac arrests across globe. Heart functional indices such as End diastolic volume, systolic volume and Ejection Fraction are usually computed from manual segmentation of left ventricle wall (LV) in short axis MR images. This is very time consuming and suffers inter-intra observer variation due to change in shape, size and contrast of LV wall in a cardiac cycle. In this paper, deformable models are improved as compared to existing approaches with respect to their adaptation on the number of iterations per image by automatic stopping of curve evolution. This has reduced the time consumption for segmentation. The proposed strategy is based on the integrated approach of machine learning and adaptive deformable flow model with no user interaction for LV initialization. CNN is used for LV detection and shape inference which makes the approach fully automatic. The proposed methodology has improved the existing parametric deformable models in terms of the time consumption and accuracy. The proposed integrated framework has significantly improved the segmentation of LV wall for better clinical inference. For assessing the accuracy of segmentation between proposed method and ground truth, validation metrics are calculated such as percentage of good contours, Dice Metric, APD, Conformity coefficient, Pearson’s Coefficient for EDV and ESV.
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References
Alzu’bi A, Amira A, Ramzan N (2016) Compact root bilinear CNN’s for content-based image retrieval. In: International conference on image, vision, and computing (ICIVC). IEEE, pp 41–45. https://doi.org/10.1109/ICIVC.2016.7571271
Adalsteinsson D, Sethian JA (1999) The fast construction of extension velocities in level set methods. J Comput Phys 148(1):2–22. https://doi.org/10.1006/jcph.1998.6090
Anh Ngo T, Carneiro G (2014) Fully automated non-rigid segmentation with distance regularized level set evolution initialized and constrained by deep-structured inference. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3118–3125. https://doi.org/10.1109/CVPR.2014.399
Assen HC, Danilouchkine MG, Frangi AF, Ords S, Westenberg JJ, Reiber JH, Lelieveldt BP (2006) Spasm: a 3D-ASM for segmentation of sparse and arbitrarily oriented cardiac MRI data. Med Image Anal 10:286–303. https://doi.org/10.1016/j.media.2005.12.001
Badrinarayanan V, Kendall A, Cipolla R (2017) Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell 39(12):2481–2495. https://doi.org/10.1109/TPAMI.2016.2644615
Ben Ayed I, Li S, Ross I (2009) Embedding overlap priors in variational left ventricle tracking. IEEE Trans Med Imaging 28:1902–1913. https://doi.org/10.1109/TMI.2009.2022087
Benjamin EJ, Virani SS, Callaway CW, Chamberlain AM, Chang AR, Cheng S, Chiuve SE, Cushman M, Delling FN, Deo R et al (2018) Heart disease and stroke statistics-2018 update: a report from the American heart association. Circulation 137(12):e67. https://doi.org/10.1161/CIR.0000000000000558
Billet F, Sermesant M, Delingette H, Ayache N (2009) Cardiac motion recovery and boundary conditions estimation by coupling an electromechanical model and cine-MRI data. In: Ayache N, Delingette H, Sermesant M (eds) Functional imaging and modeling of the heart. FIMH 2009. Lecture notes in computer science, vol 5528. Springer, Berlin, Heidelberg, pp 376–385. https://doi.org/10.1007/978-3-642-01932-6_41
Chang H-H, Zhuang AH, Valentino DJ, Chu W-C (2009) Performance measure characterization for evaluating neuroimage segmentation algorithms. Neuroimage 47(1):122–135. https://doi.org/10.1016/j.neuroimage.2009.03.068
Constantinidès C, Roullot E, Lefort M, Frouin F (2012) Fully automated segmentation of the left ventricle applied to cine MR images: description and results on a database of 45 subjects. In: Annual international conference of the IEEE engineering in medicine and biology society. IEEE, pp 3207–3210. https://doi.org/10.1109/EMBC.2012.6346647
De Alexandria AR, Cortez PC, Bessa JA, da Silva Félix JH, De Abreu JS, De Albuquerque VHC (2014) psnakes: a new radial active contour model and its application in the segmentation of the left ventricle from echocardiographic images. Comput Methods Progr Biomed 116(3):260–273. https://doi.org/10.1016/j.cmpb.2014.05.009
Dong Y, Du B, Zhang L (2015) Target detection based on random forest metric learning. IEEE J Select Top Appl Earth Observ Remote Sens 8(4):1830–1838. https://doi.org/10.1109/JSTARS.2015.2416255
Eslami A, Karamalis A, Katouzian A, Navab N (2013) Segmentation by retrieval with guided random walks: application to left ventricle segmentation in MRI. Med Image Anal 17(2):236–253. https://doi.org/10.1016/j.media.2012.10.005
Frangi AF, Niessen W, Viergever M (2001) Three-dimensional modeling for functional analysis of cardiac images, a review. IEEE Trans Med Imaging 20:2–5. https://doi.org/10.1109/42.906421
Geremia E, Clatz O, Menze BH, Konukoglu E, Criminisi A, Ayache N (2011) Spatial decision forests for MS lesion segmentation in multichannel magnetic resonance images. NeuroImage 57:378–390. https://doi.org/10.1016/j.neuroimage.2011.03.080
Heiberg E, Engblom H, Engvall J, Hedström E, Ugander M, Arheden H (2005) Semi-automatic quantification of myocardial infarction from delayed contrast enhanced magnetic resonance imaging. Scand Cardiovasc J 39(5):267–275. https://doi.org/10.1080/14017430500340543
Heiberg E, Sjögren J, Ugander M, Carlsson M, Engblom H, Arheden H (2010) Design and validation of segment-freely available software for cardiovascular image analysis. BMC Med Imaging 10(1):1–13. https://doi.org/10.1186/1471-2342-10-1
Heimann T, Meinzer HP (2009) Statistical shape models for 3d medical image segmentation: a review. Med Image Anal 13:543. https://doi.org/10.1016/j.media.2009.05.004
Hou Z, Hu Q, Nowinski WL (2006) On minimum variance thresholding. Pattern Recogn Lett 27(14):1732–1743. https://doi.org/10.1016/j.patrec.2006.04.012
Hu H, Liu H, Gao Z, Huang L (2013) Hybrid segmentation of left ventricle in cardiac MRI using gaussian-mixture model and region restricted dynamic programming. Magn Reson Imaging 31(4):575–584. https://doi.org/10.1016/j.MRI.2012.10.004
Hu H, Pan N, Wang J, Yin T, Ye R (2019) Automatic segmentation of left ventricle from cardiac MRI via deep learning and region constrained dynamic programming. Neurocomputing 347:139–148. https://doi.org/10.1016/j.neucom.2019.02.008
Huang S, Liu J, Lee LC, Venkatesh SK, Teo LLS, Au C, Nowinski WL (2011) An image-based comprehensive approach for automatic segmentation of left ventricle from cardiac short axis cine MR images. J Digit Imaging 24:598–608. https://doi.org/10.1007/s10278-010-9315-4
Jolly MP (2009) Fully automatic left ventricle segmentation in cardiac cine MR Images using registration and minimum surfaces. In: MICCAI 2009 workshop on cardiac MR left ventricle segmentation challenge, MIDAS journal. https://doi.org/10.54294/aidt6e
Kass M, Witkin A, Terzopoulos D (1988) Snakes: active contour models. Int J Comput vis 1(4):321–331. https://doi.org/10.1007/BF00133570
Kaus MR, Von Berg J, Weese J, Niessen W, Pekar V (2004) Automated segmentation of the left ventricle in cardiac MRI. Med Image Anal 8(3):245–254. https://doi.org/10.1016/j.media.2004.06.015
Lan Y, Jin R (2019) Automatic segmentation of left ventricle from cardiac MRI using deep learning and double snake model. IEEE Access 7:128641–128650. https://doi.org/10.1109/ACCESS.2019.2939542
Lee H-Y, Codella NC, 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. IEEE Trans Biomed Eng 57(4):905–913. https://doi.org/10.1109/TBME.2009.2014545
Lempitsky V, Verhoek M, Noble JA, Blake A (2009) Random forest classification for automatic delineation of myocardium in real-time 3D echocardiography. In: Ayache N, Delingette, H, Sermesant M (eds) Functional imaging and modeling of the heart. FIMH 2009. Lecture notes in computer science, vol 5528. Springer, Berlin, Heidelberg, pp 447–456. https://doi.org/10.1007/978-3-642-01932-6_48
Liu H, Hu H, Xu X, Song E (2012) Automatic left ventricle segmentation in cardiac MRI using topological stable-state thresholding and region restricted dynamic programming. Acad Radiol 19(6):723–731. https://doi.org/10.1016/j.acra.2012.02.011
Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3431–3440. https://doi.org/10.48550/arXiv.1411.4038
Lorenzo-Valdés M, Sanchez-Ortiz GI, Elkington AG, Mohiaddin RH, Rueckert D (2004) Segmentation of 4D cardiac MR images using a probabilistic atlas and the EM algorithm. Med Image Anal 8:255–265. https://doi.org/10.1016/j.media.2004.06.005
Lynch M, Ghita O, Whelan PF (2006) Automatic segmentation of the left ventricle cavity and myocardium in MRI data. Comput Bio Med 36(4):389–407. https://doi.org/10.1016/j.compbiomed.2005.01.005
Margeta J, Geremia E, Criminisi A, Ayache N (2012) 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) Statistical atlases and computational models of the Heart. Imaging and modelling challenges. STACOM 2011. Lecture notes in computer science, vol 7085. Springer, Berlin, Heidelberg, pp 109–119. https://doi.org/10.1007/978-3-642-28326-0_11
Masci J, Giusti A, Ciresan D, Fricout G, Schmidhuber J (2014). A fast learning algorithm for image segmentation with max-pooling convolutional networks. In: IEEE International conference on image processing, pp 2713–2717. https://doi.org/10.48550/arXiv.1302.1690
MICCAI 2009 segmentation challenge. http://smial.sri.utoronto.ca/LV_Challenge/Data.html
Nambakhsh CM, Yuan J, Punithakumar K, Goela A, Rajchl M, Peters TM, Ayed IB (2013) Left ventricle segmentation in MRI via convex relaxed distribution matching. Med Image Anal 17(8):1010–1024. https://doi.org/10.1016/j.media.2013.05.002
Ngo TA, Lu Z, Carneiro G (2017) Combining deep learning and level set for the automated segmentation of the left ventricle of the heart from cardiac cine magnetic resonance. Med Image Anal 35:159–171. https://doi.org/10.1016/j.media.2016.05.009
Pednekar A, Kurkure U, Muthupillai R, Flamm S, Kakadiaris IA (2006) Automated left ventricular segmentation in cardiac MRI”. IEEE Trans Biomed Eng 53(7):1425–1428. https://doi.org/10.1109/TBME.2006.873684
Petitjean C, Dacher J-N (2011) A review of segmentation methods in short axis cardiac MR images. Med Image Anal 15(2):169–184. https://doi.org/10.1016/j.media.2010.12.004
Queirós S, Barbosa D, Heyde B, Morais P, Vilaça JL, Friboulet D, Bernard O, Darhooge J (2014) Fast automatic myocardial segmentation in 4d cine CMR datasets. Med Image Anal 18(7):1115–1131. https://doi.org/10.1016/j.media.2014.06.001
Radau P, Connelly K (2009) Evaluation framework for algorithms segmenting short axis cardiac MRI. Graph Models Image Process. https://doi.org/10.54294/g80ruo
Ricci C, Wood A, Muller D, Gunter MJ, Agudo A, Boeing H, Van Der Schouw YT, Warnakula S, Saieva C, Spijkerman A et al (2018) Alcohol intake in relation to non-fatal and fatal coronary heart disease and stroke: Epic-cvd case-cohort study. BMJ 361:k934. https://doi.org/10.1136/bmj.k934
Schaerer J, Casta C, Pousin J (2010) A dynamic elastic model for segmentation and tracking of the heart in MR image sequences. Med Image Anal 6:738–749. https://doi.org/10.1016/j.media.2010.05.009
Shao L, Zhou H (1996) Curve fitting with bezier cubics. Graph Models Image Process 58(3):223–232. https://doi.org/10.1006/gmip.1996.0019
Shen D, Wu G, Suk HI (2017) Deep learning in medical image analysis. Annu Rev Biomed Eng 19:221–248. https://doi.org/10.1146/annurev-bioeng-071516-044442
Suinesiaputra A, Cowan BR, Al-Agamy AO, Elattar MA, Ayache N, Fahmy AS, Khalifa AM, Gracia PM, Jolly MP, Kadish AH, Lee DC, Margeta J, Warfield SK, Young AA (2014) A collaborative resource to build consensus for automated left ventricular segmentation of cardiac MR images. Med Image Anal 18:50–62. https://doi.org/10.1016/j.media.2013.09.001
Üzümcü M, van der Geest RJ, Swingen C, Reiber JH, Lelieveldt BP (2006) Time continuous tracking and segmentation of cardiovascular magnetic resonance images using multidimensional dynamic programming. Invest Radiol 41(1):52–62. https://doi.org/10.1097/01.rli.0000194070.88432.24
Uzunbas M, Zhang S, Pohl K (2012) Segmentation of myocardium using deformable regions and graph cuts. Proc ISBI 2012:254–257. https://doi.org/10.1109/ISBI.2012.6235532
Wang Y, Wu Y, Jia Y (2014) Shape constraints for the left ventricle segmentation from cardiac cine MRI based on snake models, Shape Analysis in Medical Image Analysis. Springer 14:373–412. https://doi.org/10.1007/978-3-319-03813-1_12
Winjhout J, Hendriksen D, Van Assen H (2009) LV challenge LKEB contribution, Fully automated myocardial contour detection. MIDAS J. https://doi.org/10.54294/xvoael
Xu X, Xu S, Jin L, Song E (2011) Characteristic analysis of otsu threshold and its applications”. Pattern Recogn Lett 32(7):956–961. https://doi.org/10.1016/j.patrec.2011.01.021
Yeh J-Y, Fu J, Wu C, Lin H, Chai J (2005) Myocardial border detection by branch-and-bound dynamic programming in magnetic resonance images. Comput Methods Programs Biomed 79(1):19–29. https://doi.org/10.1016/j.cmpb.2004.10.010
Zhang H, Wahle A, Johnson RK, Scholz TD, Sonka M (2010) 4-D cardiac MR image analysis: left and right ventricular morphology and function. IEEE Trans Med Imaging 29:350–364. https://doi.org/10.1109/TMI.2009.2030799
Zhang F, Du B, Zhang L (2014) Saliency-guided unsupervised feature learning for scene classification. IEEE Trans Geosci Remote Sens 53(4):2175–2184. https://doi.org/10.1109/TGRS.2014.2357078
Zhang W, Li R, Deng H, Wang L, Lin W, Ji S, Shen D (2015) Deep convolutional neural networks for multi-modality isointense infant brain image segmentation. Neuro Image 108:214–224. https://doi.org/10.1016/j.neuroimage.2014.12.061
Zhuang X, Hawkes DJ, Crum WR, Boubertakh R, Uribe S, Atkinson D, Batchelor P, Schaeffter T, Razavi R, Hill DLG (2008) Robust registration between cardiac MRI images and atlas for segmentation propagation. In: Proceedings of SPIE - the international society for optical engineering. https://doi.org/10.1117/12.769445
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Bhan, A., Mangipudi, P. Integrated approach for fully automatic left ventricle segmentation using adaptive iteration based parametric model with deep learning in short axis cardiac MRI. J Ambient Intell Human Comput 14, 11071–11092 (2023). https://doi.org/10.1007/s12652-022-04389-5
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DOI: https://doi.org/10.1007/s12652-022-04389-5