Marching cubes: A high resolution 3D surface construction algorithm
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Computing Geodesics and Minimal Surfaces via Graph Cuts
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Robust Real-Time Face Detection
International Journal of Computer Vision
Automatic Segmentation of the Left Ventricle in Cardiac MR and CT Images
International Journal of Computer Vision
MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
Segmentation of Left Ventricle in Cardiac Cine MRI: An Automatic Image-Driven Method
FIMH '09 Proceedings of the 5th International Conference on Functional Imaging and Modeling of the Heart
A multiview approach to tracking people in crowded scenes using a planar homography constraint
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Automatic cardiac motion tracking using both untagged and 3d tagged MR images
STACOM'11 Proceedings of the Second international conference on Statistical Atlases and Computational Models of the Heart: imaging and modelling challenges
Layered spatio-temporal forests for left ventricle segmentation from 4d cardiac MRI data
STACOM'11 Proceedings of the Second international conference on Statistical Atlases and Computational Models of the Heart: imaging and modelling challenges
A multi-image graph cut approach for cardiac image segmentation and uncertainty estimation
STACOM'11 Proceedings of the Second international conference on Statistical Atlases and Computational Models of the Heart: imaging and modelling challenges
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In this paper, we develop a framework for the automatic detection and segmentation of the ventricle and myocardium from multislice, short-axis cine MR images. The segmentation framework has the ability to deal with large shape variability of the heart, poorly defined boundaries and abnormal intensity distribution of the myocardium (e.g. due to infarcts). We integrate a series of state-of-the-art techniques into a fully automatic workflow, including a detection algorithm for the LV, atlas-based segmentation, and intensity-based refinement using a Gaussian mixture model that is optimized using the Expectation Maximization (EM) algorithm and the graph cut algorithm. We evaluate this framework on three different patient groups, one with infarction, one with left ventricular hypertrophy (both are common result of cardiovascular diseases) and another group of subjects with normal heart anatomy. Results indicate that the proposed method is capable of producing segmentation results that show good robustness and high accuracy (Dice 0.908 ± 0.025 for the endocardial and 0.946 ± 0.016 for the epicardial segmentations) across all patient groups with and without pathology.