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

March 8, 2019

Xiuquan DuSusu YinRenjun TangYanping ZhangShuo Li

Early Access Note:
Early Access articles are new content made available in advance of the final electronic or print versions and result from IEEE’s Preprint or Rapid Post processes. Preprint articles are peer-reviewed but not fully edited. Rapid Post articles are peer-reviewed and edited but not paginated. Both these types of Early Access articles are fully citable from the moment they appear in IEEE Xplore.

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 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 that 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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