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Left ventricle segmentation from cardiac MRI combining level set methods with deep belief networks

This paper introduces a new semi-automated methodology combining a level set method with a top-down segmentation produced by a deep belief network for the problem of left ventricle segmentation from cardiac magnetic resonance images (MRI). Our approach combines the level set advantages that uses several a priori facts about the object to be segmented (e.g., smooth contour, strong edges, etc.) with the knowledge automatically learned from a manually annotated database (e.g., shape and appearance of the object to be segmented). The use of deep belief networks is justified because of its ability to learn robust models with few annotated images and its flexibility that allowed us to adapt it to a top-down segmentation problem. We demonstrate that our method produces competitive results using the database of the MICCAI grand challenge on left ventricle segmentation from cardiac MRI images, where our methodology produces results on par with the best in the field in each one of the measures used in that challenge (perpendicular distance, Dice metric, and percentage of good detections). Therefore, we conclude that our proposed methodology is one of the most competitive approaches in the field.