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.
Correlated Regression Feature Learning for Automated Right Ventricle Segmentation
Accurate segmentation of right ventricle (RV) from cardiac magnetic resonance (MR) images can help doctor to robustly quantify the clinical indices including ejection fraction. In this paper, we develop one regression convolutional neural network (RegressionCNN) that a holistic regression model is incorporated with convolutional neural network (CNN) to determine boundary points’ coordinates of RV directly and simultaneously.