scholarly journals Left Ventricle Contouring in Cardiac Images Based on Deep Reinforcement Learning

2021 ◽  
Author(s):  
Sixing Yin ◽  
Yameng Han ◽  
Judong Pan ◽  
YIning Wang ◽  
Shufang Li ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Left Ventricle ◽  
Drawing Process ◽  
Path Finding ◽  
Cardiac Image ◽  
Cardiac Images

<pre> In this paper, we propose a novel reinforcement-learning-based framework for left ventricle contouring, which mimics how a cardiologist outlines the left ventricle in a cardiac image. Since such a contour drawing process is simply moving a paintbrush along a specific trajectory, it is thus analogized to a path finding problem.</pre>

scholarly journals Left Ventricle Contouring in Cardiac Images Based on Deep Reinforcement Learning

2021 ◽  
Author(s):  
Sixing Yin ◽  
Yameng Han ◽  
Judong Pan ◽  
YIning Wang ◽  
Shufang Li ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Left Ventricle ◽  
Drawing Process ◽  
Path Finding ◽  
Cardiac Image ◽  
Cardiac Images

<pre> In this paper, we propose a novel reinforcement-learning-based framework for left ventricle contouring, which mimics how a cardiologist outlines the left ventricle in a cardiac image. Since such a contour drawing process is simply moving a paintbrush along a specific trajectory, it is thus analogized to a path finding problem.</pre>

Segmenting and Tracking the Left Ventricle by Learning the Dynamics in Cardiac Images

2005 ◽  
pp. 553-565 ◽  
Author(s):  
W. Sun ◽  
M. Çetin ◽  
R. Chan ◽  
V. Reddy ◽  
G. Holmvang ◽  
...  
Keyword(s):  
Left Ventricle ◽  
Cardiac Images

scholarly journals Segmentation of the Left Ventricle from Cine MR Images Using a Comprehensive Approach

2009 ◽  
Author(s):  
Su Huang ◽  
Jimin Liu ◽  
Looi Chow Lee ◽  
Sudhakar K Venkatesh ◽  
Lynette Li San Teo ◽  
...  
Keyword(s):  
Left Ventricle ◽  
Gold Standard ◽  
Practical Method ◽  
Comprehensive Approach ◽  
Mr Images ◽  
Multiple Image ◽  
Cardiac Image ◽  
Cine Mr ◽  
Segmentation Methods ◽  
Cardiac Image Segmentation

Segmentation of the left ventricle is important in assessment of cardiac functional parameters. Currently, manual segmentation is the gold standard for acquiring these parameters and can be time-consuming. Therefore, accuracy and automation are two important criteria in improving cardiac image segmentation methods. In this paper, we present a comprehensive approach that utilizes various features of cine MR images and combines multiple image processing methods including thresholding, edge detection, mathematical morphology, deformable model as well as image filtering. The segmentation is performed automatically with minimized interaction to optimize segmentation results. This approach provides cardiac radiologists a practical method for accurate segmentation of the left ventricle.

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