Residual learning: A new paradigm to improve deep learning-based segmentation of the left ventricle in magnetic resonance imaging cardiac images

Maral Zarvani, Sara Saberi, Reza Azimi, Seyed Vahab Shojaedini

DOI: 10.4103/jmss.JMSS_38_20

Abstract


Background: Recently, magnetic resonance imaging (MRI) has become a useful tool for the early detection of heart failure. A vital step of this process is a valid measurement of the left ventricle's properties, which seriously depends on the accurate segmentation of the heart in captured images. Although various schemes have been tested for this segmentation so far, the latest proposed methods have used the concept of deep learning to estimate the range of the left ventricle in cardiac MRI images. While deep learning methods can lead to better results than their classical alternatives, but unfortunately, the gradient vanishing and exploding problems may hamper their efficiency for the accurate segmentation of the left ventricle in MRI heart images. Methods: In this article, a new concept called residual learning is utilized to improve the performance of deep learning schemes against gradient vanishing problems. For this purpose, the Residual Network of Residual Network (i.e., Residual of Residual) substructure is utilized inside the main deep learning architecture (e.g., Unet), which provides more significant detection indexes. Results and Conclusion: The proposed method's performances and its alternatives were evaluated on Sunnybrook Cardiac Data as a reliable dataset in the left ventricle segmentation. The results show that the detection parameters are improved at least by 5%, 3.5%, 8.1%, and 11.4% compared to its deep alternatives in terms of Jaccard, Dice, precision, and false-positive rate indexes, respectively. These improvements were made when the recall parameter was reduced to a negligible value (i.e., approximately 1%). Overall, the proposed method can be used as a suitable tool for more accurate detection of the left ventricle in MRI images.

Keywords


Deep learning, left ventricle, magnetic resonance imaging, residual learning, semantic segmentation

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References


Peterzan MA, Rider OJ, Anderson LJ. The role of cardiovascular magnetic resonance imaging in heart failure. Cardiac Fail Rev 2016;2:115.

Mazaheri S, Sulaiman PSB, Wirza R, Khalid F, Kadiman S, Dimon MZ, et al. Echocardiography image segmentation: A survey. In: 2013 International Conference on Advanced Computer Science Applications and Technologies. Kuching, Malaysia: IEEE; 2013.

Merjulah R, Chandra J. Segmentation technique for medical image processing: A survey. In: 2017 International Conference on Inventive Computing and Informatics (ICICI). Coimbatore, India: IEEE; 2017. Back to cited text no. 3

Wolterink JM. Left ventricle segmentation in the era of deep learning. In: Virginia, Springer: Springer; 2019.

Petitjean C, Dacher JN. A review of segmentation methods in short axis cardiac MR images. Med Image Anal 2011;15:169-84.

Goshtasby A, Turner DA. Segmentation of cardiac cine MR images for extraction of right and left ventricular chambers. IEEE Trans Med Imaging 1995;14:56-64.

Kass M, Witkin A, Terzopoulos D. Snakes: Active contour models. Int J Comput Vis 1988;1:321-31.

Avendi MR, Kheradvar A, Jafarkhani H. A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI. Med Image Anal 2016;30:108-19.

Metaxas DN, Yan Z. Deformable models, sparsity and learning-based segmentation for cardiac MRI based analytics. In: Handbook of Medical Image Computing and Computer Assisted Intervention. Cambridge, Massachusetts, United States: Elsevier; 2020. p. 273-92.

Van Der Geest RJ, Lelieveldt BP, Angelié E, Danilouchkine M, Swingen C, Sonka M, et al. Evaluation of a new method for automated detection of left ventricular boundaries in time series of magnetic resonance images using an active appearance motion model. J Cardiovasc Magn Reson 2004;6:609-17.

Mitchell SC, Lelieveldt BP, van der Geest RJ, Bosch HG, Reiber JH, Sonka M. Multistage hybrid active appearance model matching: Segmentation of left and right ventricles in cardiac MR images. IEEE Trans Med Imaging 2001;20:415-23.

Cootes TF, Hill A, Taylor CJ, Haslam J. Use of active shape models for locating structures in medical images. Image Vis Comput 1994;12:355-65.

Lorenzo-Valdés M, Sanchez-Ortiz GI, Elkington AG, Mohiaddin RH, Rueckert D. Segmentation of 4D cardiac MR images using a probabilistic atlas and the EM algorithm. Med Image Anal 2004;8:255-6.

Zhuang X, Hawkes DJ, Crum WR, Boubertakh R, Uribe S, Atkinson D, et al. Robust registration between cardiac MRI images and atlas for segmentation propagation. In: Medical Imaging 2008: Image Processing. California, United States: International Society for Optics and Photonics; 2008.

Iglesias JE, Sabuncu MR. Multi-atlas segmentation of biomedical images: A survey. Med Image Anal 2015;24:205-19.

Carneiro G, Nascimento JC, Freitas A. The segmentation of the left ventricle of the heart from ultrasound data using deep learning architectures and derivative-based search methods. IEEE Trans Image Process 2011;21:968-82.

Romaguera LV, Vázquez L, Costa MG, Romero FP, Filho CF. Left ventricle segmentation in cardiac MRI images using fully convolutional neural networks. In: Medical Imaging 2017: Computer-Aided Diagnosis. Orlando, Florida, United States: International Society for Optics and Photonics; 2017.

Leclerc S, Sarah, Smistad E, Grenier T, Lartizien C, Ostvik A, et al. Deep learning applied to multi-structure segmentation in 2D echocardiography: A preliminary investigation of the required database size. In: 2018 IEEE International Ultrasonics Symposium (IUS). Kobe, Japan: IEEE; 2018.

Zyuzin V, Chumarnaya T. Comparison of unet architectures for segmentation of the left ventricle endocardial border on two-dimensional ultrasound images. In: 2019 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT). Yekaterinburg, Russia: IEEE; 2019.

Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition.arXiv;2014:1409.1556.

He K, Zhang X, Ren S, Sun J. Identity mappings in deep residual networks. In: European Conference on Computer Vision. Amsterdam, Netherlands: Springer; 2016.

Litjens G, Kooi T, Bejnordi BE, Setio AA, Ciompi F, Ghafoorian M, et al. A survey on deep learning in medical image analysis. Med Image Anal 2017;42:60-88.

Yan W, Wang Y, Li Z, Geest RJ, Tao Q. Left ventricle segmentation via optical-flow-net from short-axis cine MRI: Preserving the temporal coherence of cardiac motion. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Granada, Spain: Springer; 2018.

Chen C, Qin C, Qiu H, Tarroni G, Duan J, Bai W, et al. Deep learning for cardiac image segmentation: A review. Front Cardiovasc Med 2020;7:25.

Zhang D, Dongqing, Icke I, Dogdas B, Parimal S, Sampath S, et al. A multi-level convolutional LSTM model for the segmentation of left ventricle myocardium in infarcted porcine cine MR images. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). Washington, DC, USA: IEEE; 2018.

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. Munich, Germany: Springer; 2015.

He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Montreal, Canada; 2016.

Ji Q, Huang J, He W, Sun Y. Optimized deep convolutional neural networks for identification of macular diseases from optical coherence tomography images. Algorithms 2019;12:51.

Radau P, Lu Y, Connelly K, Paul G, Dick AJ, Wright G. Evaluation framework for algorithms segmenting short axis cardiac MRI. The MIDAS Journal-Cardiac MR Left Ventricle Segmentation Challenge 49 (2009).

Abdelmaguid E, Huang J, Kenchareddy S, Singla D, Wilke L, Nguyen MH, et al. Left ventricle segmentation and volume estimation on cardiac mri using deep learning. arXiv;2018:1809.06247.

Chen A, Antong, Zhou T, Icke I, Parimal S, Dogdas B, et al. Transfer learning for the fully automatic segmentation of left ventricle myocardium in porcine cardiac cine MR images. In: International Workshop on Statistical Atlases and Computational Models of the Heart. Quebec City, Canada: Springer; 2017.

Chen M, Fang L, Liu H. FR-NET: Focal loss constrained deep residual networks for segmentation of cardiac MRI. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). Venice, Italy: IEEE; 2019.

Taha AA, Hanbury A. Metrics for evaluating 3D medical image segmentation: Analysis, selection, and tool. BMC Med Imaging 2015;15:29.

Zhang J, Du J, Liu H, Hou X, Zhao Y, Ding M. LU-NET: An Improved U-Net for ventricular segmentation. IEEE Access 2019;7:92539-46.

Kaus MR, von Berg J, Weese J, Niessen W, Pekar V. Automated segmentation of the left ventricle in cardiac MRI. Med Image Anal 2004;8:245-54.


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