Cardiac-DeepIED: Automatic Pixel-Level Deep Segmentation for Cardiac Bi-Ventricle Using Improved End-to-End Encoder-Decoder Network

Accurate segmentation of cardiac bi-ventricle (CBV) from magnetic resonance (MR) images has a great significance to analyze and evaluate the function of the cardiovascular system. However, the complex structure of CBV image makes fully automatic segmentation as a well-known challenge. In this paper,...

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Vydané v:IEEE journal of translational engineering in health and medicine Ročník 7; s. 1 - 10
Hlavní autori: Du, Xiuquan, Yin, Susu, Tang, Renjun, Zhang, Yanping, Li, Shuo
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.01.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Architecture
Automation
Cardiovascular system
CBV segmentation
Coders
Convolution
Decoding
deep learning
Embedded systems
encoder-decoder
Encoders-Decoders
Fires
Image segmentation
magnetic resonance images
Magnetic resonance imaging
Modules
Myocardium
Pixels
Regional analysis
Semantics
Task analysis
Wall thickness
deep learning
magnetic resonance images
encoder-decoder
CBV segmentation
ISSN:2168-2372, 2168-2372
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Shrnutí:Accurate segmentation of cardiac bi-ventricle (CBV) from magnetic resonance (MR) images has a great significance to analyze and evaluate the function of the cardiovascular system. However, the complex structure of CBV image makes fully automatic segmentation as a well-known challenge. In this paper, we propose an improved end-to-end encoder-decoder network for CBV segmentation from the pixel level view (Cardiac-DeepIED). In our framework, we explicitly solve the high variability of complex cardiac structures through an improved encoder-decoder architecture which consists of Fire dilated modules and D-Fire dilated modules. This improved encoder-decoder architecture has the advantages of being capable of obtaining semantic task-aware representation and preserving fine-grained information. In addition, our method can dynamically capture potential spatiotemporal correlations between consecutive cardiac MR images through specially designed convolutional long-term and short-term memory structure; it can simulate spatiotemporal contexts between consecutive frame images. The combination of these modules enables the entire network to get an accurate, robust segmentation result. The proposed method is evaluated on the 145 clinical subjects with leave-one-out cross-validation. The average dice metric (DM) is up to 0.96 (left ventricle), 0.89 (myocardium), and 0.903 (right ventricle). The performance of our method outperforms state-of-the-art methods. These results demonstrate the effectiveness and advantages of our method for CBV regions segmentation at the pixel-level. It also reveals the proposed automated segmentation system can be embedded into the clinical environment to accelerate the quantification of CBV and expanded to volume analysis, regional wall thickness analysis, and three LV dimensions analysis.
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ISSN:2168-2372
2168-2372
DOI:10.1109/JTEHM.2019.2900628