Abstract
In this paper, the segmentation model is developed using the convolutional neural network for automatic segmentation of a right ventricle MRI image. The proposed model is trained end-to-end using an RVSC dataset that contains the right ventricle magnetic resonance images. The proposed model gives state-of-art achievement for dice metric and also for the Jaccard index. The proposed model achieves an optimal model performance of dice metric performance with 0.91 (0.10) for the training dataset and 0.88 (0.12) for the validation dataset.
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World Health Organization: Cardiovascular disease. Retrieved from World Health Organization: https://www.who.int/cardiovascular_diseases/about_cvd/en (2019)
Vision X-ray group: Retrieved from the importance of medical imaging: https://www.xray.com.au/importance-of-medical-imaging/ (2013, November 15)
Magnetic Resonance Imaging: Retrieved from Main Line Health. https://www.mainlinehealth.org/conditions-and-treatments/treatments/magnetic-resonance-imaging (2019)
Shankar, V.; Kumar, V.; Devagade, U., et al.: Heart disease prediction using CNN algorithm. SN Comput. Sci. 1, 170 (2020). https://doi.org/10.1007/s42979-020-0097-6
Cois, A.; Galeotti, J.; Tamburo, R.; Sacks, M.; Stetten, G.: Shells and spheres: an n-dimensional framework for medial-based image segmentation. Int. J. Biomed. Imaging 2010, 980872 (2010). https://doi.org/10.1155/2010/980872
Xie, L., et al.: Multi-atlas label fusion with augmented atlases for fast and accurate segmentation of cardiac MR images. In: 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI). IEEE (2015)
Guo, Z.-Z., et al.: Local Motion Intensity Clustering (LMIC) model for segmentation of right ventricle in cardiac MRI images. IEEE J. Biomed. Health Inform. 23(2), 723–730 (2018)
Chang, Y., et al.: Automatic segmentation and cardiopathy classification in cardiac MRI images based on deep neural networks. In: 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP). IEEE (2018)
Gupta, A., et al.: Cardiac MR image segmentation using deformable models. In: Computers in Cardiology 1993, Proceedings. IEEE (1993).
Krasnopevtsev, P., et al.: A statistical approach to automatic heart segmentation and modelling from multiple modalities. In: 21st IEEE International Symposium on Computer-Based Medical Systems, 2008. CBMS'08. IEEE (2008).
Constantinides, C., et al.: Semi-automated cardiac segmentation on cine magnetic resonance images using GVF-Snake deformable models. MIDAS J. Cardiac MR Left Ventricle Segment. Challenge (2009)
Casta, C., et al. Evaluation of the dynamic deformable elastic template model for the segmentation of the heart in MRI sequences. MIDAS J. Cardiac MR Left Ventricle Segment. Challenge (2009)
Santiago, C.; Nascimento, J.C.; Marques, J.S.: Segmentation of the left ventricle in cardiac MRI using a probabilistic data association active shape model. In: Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE. IEEE (2015)
Yang, X.L., et al.: Automatic segmentation of left ventricular myocardium by deep convolutional and de-convolutional neural networks. In: Computing in Cardiology Conference (CinC), 2016. IEEE (2016)
Ronneberger, O.; Fischer, P.; Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention. Springer, Cham (2015)
univ-rouen.fr., caroline.petitjean at. litis. 2012. 2019. http://www.litislab.fr/?projet=1rvsc
Zou, K.H., et al.: Statistical validation of image segmentation quality based on a spatial overlap index1: scientific reports. Acad. Radiol. 11(2), 178–189 (2004)
Shi, R.; Ngan, K.N.; Li, S.: Jaccard index compensation for object segmentation evaluation. In: 2014 IEEE International Conference on Image Processing (ICIP). IEEE (2014)
Tran, P.V.: A fully convolutional neural network for cardiac segmentation in short axis MRI (2016)
Avendi, M.R.; Kheradvar, A.; Jafarkhani, H.: Automatic segmentation of the right ventricle from cardiac MRI using a learning-based approach. Magn. Reson. Med. 78(6), 2439–2448 (2017). https://doi.org/10.1002/mrm.26631
Petitjean, C.; Zuluaga, M.A.; Bai, W.; Dacher, J.N.; Grosgeorge, D.; Caudron, J., et al.: Right ventricle segmentation from cardiac MRI: a collation study. Med. Image Anal. 19(1), 187–202 (2015). https://doi.org/10.1016/j.media.2014.10.004
Oktay, O.et al.: Anatomically constrained neural networks (ACNNs): application to cardiac image enhancement and segmentation. IEEE Trans. Med. Imaging 37(2), 384–395 (2018)
Chitiboi, T., et al.: Context-based segmentation and analysis of multi-cycle real-time cardiac MRI. In: 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI). IEEE (2014)
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Dharwadkar, N.V., Savvashe, A.K. Right Ventricle Segmentation of Magnetic Resonance Image Using the Modified Convolutional Neural Network. Arab J Sci Eng 46, 3713–3722 (2021). https://doi.org/10.1007/s13369-020-05309-5
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DOI: https://doi.org/10.1007/s13369-020-05309-5