A combined multi-scale deep learning and random forests approach for direct left ventricular volumes estimation in 3D echocardiography

Computing in Cardiology

Volumn 43, Issue , 2016, Pages 889-892

  Dong, Suyu ^a   Luo, Gongning ^a   Sun, Guanxiong ^a   Wang, Kuanquan ^a   Zhang, Henggui ^a,b  

^a HARBIN INSTITUTE OF TECHNOLOGY   (China)

^b UNIVERSITY OF MANCHESTER   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

CARDIOLOGY; CONVOLUTION; DECISION TREES; DIAGNOSIS; ECHOCARDIOGRAPHY;

3D ECHOCARDIOGRAPHY; CARDIAC FUNCTION ANALYSIS; ECHOCARDIOGRAPHY IMAGES; ESTIMATION METHODS; IRREGULAR GEOMETRIES; LEARNING FRAMEWORKS; LEFT VENTRICULAR FUNCTION; LEFT VENTRICULAR VOLUME ESTIMATION;

DEEP LEARNING;

EID: 85016124927     PISSN: 23258861     EISSN: 2325887X     Source Type: Conference Proceeding    
DOI: 10.22489/cinc.2016.258-250     Document Type: Conference Paper

References (17)

1. De Alexandria AR, Cortez PC, Bessa JA, Felix JHD, de Abreu JS, de Albuquerque VHC. pSnakes: A new radial active contour model and its application in the segmentation of the left ventricle from echocardiographic images. Comput Meth Prog Bio. 2014; 116(3): 260-73.
2. Dietenbeck T, Barbosa D, et al. Whole myocardium tracking in 2D-echocardiography in multiple orientations using a motion constrained level-set. Med Image Anal. 2014; 18(3): 500-14.
3. Saini K, Dewal ML, Rohit M. A Fast Region-Based Active Contour Model for Boundary Detection of Echocardiographic Images. J Digit Imaging. 2012; 25(2): 271-8.
4. Kass M WA, Terzopoulos D. Snakes: Active contour models. International journal of computer vision. 1988; 1(4): 321-31.
5. Cootes T F TCJ, Cooper D H, et al. Active shape modelstheir training and application. Computer vision and image understanding. 1995; 61(1): 38-59.
6. L B. Random forests. Machine learning. 2001; 45(1): 5-32.
7. Georgescu B ZXS, Comaniciu D, et al. Database-guided segmentation of anatomical structures with complex appearance. IEEE Computer Vision and Pattern Recognition (CVPR'05):; 2005. p. 429-36.
8. Barbosa D, Dietenbeck T, et al. Fast and fully automatic 3-d echocardiographic segmentation using B-spline explicit active surfaces: feasibility study and validation in a clinical setting. Ultrasound Med Biol. 2013; 39(1): 89-101.
9. Lin N, Yu W, Duncan JS. Combinative multi-scale level set framework for echocardiographic image segmentation. Med Image Anal. 2003; 7(4): 529-37.
10. Dietenbeck T, Alessandrini M, Barbosa D, D'hooge J, Friboulet D, Bernard O. Detection of the whole myocardium in 2D-echocardiography for multiple orientations using a geometrically constrained level-set. Med Image Anal. 2012; 16(2): 386-401.
11. Hansegård J OF, Rabben S I. Real-time active shape models for segmentation of 3D cardiac ultrasound. International Conference on Computer Analysis of Images and Patterns: Springer Berlin Heidelberg; 2007. p. 157-64.
12. Mitchell SC, Lelieveldt BP, et al. 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.
13. Barbosa D, Dietenbeck T, Schaerer J, D'Hooge J, Friboulet D, Bernard O. B-spline explicit active surfaces: An efficient framework for real-time 3-D region-based segmentation. IEEE transactions on image processing 2012; 21(1): 241-51.
14. 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 T Image Process. 2012; 21(3): 968-82.
15. Luo GN, An R, Wang KQ, Dong SY, Zhang HG. A deep learning network for right ventricle segmentation in shortaxis MRI. Computing in Cardiology: IEEE; 2016.
16. Luo GN, Sun GX, et al. A novel left ventricular volumes prediction method based on deep learning network in cardiac MRI. Computing in Cardiology: IEEE; 2016.
17. Lee H GR, Ranganath R, et al. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. Proceedings of the 26th annual international conference on machine learning ACM 2009. p. 609-16.