A Combined Fully Convolutional Networks and Deformable Model for Automatic Left Ventricle Segmentation Based on 3D Echocardiography

Author:

Dong Suyu1,Luo Gongning1,Wang Kuanquan1ORCID,Cao Shaodong2,Li Qince1,Zhang Henggui134ORCID

Affiliation:

1. School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China

2. Department of Radiology, The Fourth Hospital of Harbin Medical University, Harbin 150001, China

3. School of Physics and Astronomy, University of Manchester, Manchester, UK

4. Space Institute of Southern China, Shenzhen, Guangdong, China

Abstract

Segmentation of the left ventricle (LV) from three-dimensional echocardiography (3DE) plays a key role in the clinical diagnosis of the LV function. In this work, we proposed a new automatic method for the segmentation of LV, based on the fully convolutional networks (FCN) and deformable model. This method implemented a coarse-to-fine framework. Firstly, a new deep fusion network based on feature fusion and transfer learning, combining the residual modules, was proposed to achieve coarse segmentation of LV on 3DE. Secondly, we proposed a method of geometrical model initialization for a deformable model based on the results of coarse segmentation. Thirdly, the deformable model was implemented to further optimize the segmentation results with a regularization item to avoid the leakage between left atria and left ventricle to achieve the goal of fine segmentation of LV. Numerical experiments have demonstrated that the proposed method outperforms the state-of-the-art methods on the challenging CETUS benchmark in the segmentation accuracy and has a potential for practical applications.

Funder

National Natural Science Foundation of China

Publisher

Hindawi Limited

Subject

General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,General Medicine

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