Digital Image Processing
Interactive Organ Segmentation Using Graph Cuts
MICCAI '00 Proceedings of the Third International Conference on Medical Image Computing and Computer-Assisted Intervention
Comprehensive Segmentation of Cine Cardiac MR Images
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Left Ventricle Tracking Using Overlap Priors
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Automated detection of left ventricle in 4d MR images: experience from a large study
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
A multiresolution image segmentation technique based on pyramidal segmentation and fuzzy clustering
IEEE Transactions on Image Processing
Pattern Recognition of Abnormal Left Ventricle Wall Motion in Cardiac MR
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
FIMH'11 Proceedings of the 6th international conference on Functional imaging and modeling of the heart
VURTIGO: visualization platform for real-time, MRI-Guided cardiac electroanatomic mapping
STACOM'11 Proceedings of the Second international conference on Statistical Atlases and Computational Models of the Heart: imaging and modelling challenges
STACOM'12 Proceedings of the third international conference on Statistical Atlases and Computational Models of the Heart: imaging and modelling challenges
Fast fully automatic segmentation of the myocardium in 2D cine MR images
FIMH'13 Proceedings of the 7th international conference on Functional Imaging and Modeling of the Heart
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This study investigates a fully automatic left ventricle segmentation method from cine short axis MR images. Advantages of this method include that it: 1) does not require manually drawn initial contours, trained statistical shape or gray-level appearance model; 2) provides not only endocardial and epicardial contours, but also papillary muscles and trabeculations' contours; 3) introduces a roundness measure that is fast and automatically locates the left ventricle; 4) simplifies the epicardial contour segmentation by mapping the pixels from Cartesian to approximately polar coordinates; and 5) applies a fast Fourier transform to smooth the endocardial and epicardial contours. Quantitative evaluation was performed on 41 subjects. The average perpendicular distance between manually drawn and automatically selected contours over all slices, all studies, and two phases (end-diastole and end-systole) was 1.40±1.18 mm for endocardial and 1.75±1.15 mm for epicardial contours. These results indicate a promising method for automatic segmentation of left ventricle for clinical use.