A fast algorithm for active contours and curvature estimation
CVGIP: Image Understanding
A Variational Approach for the Segmentation of the Left Ventricle in Cardiac Image Analysis
International Journal of Computer Vision
Automatic Segmentation of the Left Ventricle in Cardiac MR and CT Images
International Journal of Computer Vision
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
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
Left Ventricle Segmentation via Graph Cut Distribution Matching
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
Collaborative tracking for MRI-guided robotic intervention on the beating heart
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part III
Cardiac MRI intervention and diagnosis via deformable collaborative tracking
FIMH'11 Proceedings of the 6th international conference on Functional imaging and modeling of the heart
Global assessment of cardiac function using image statistics in MRI
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
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A typical Cardiac Magnetic Resonance (CMR) examination includes acquisition of a sequence of short-axis (SA) and long-axis (LA) images covering the cardiac cycle. Quantitative analysis of the heart function requires segmentation of the left ventricle (LV) SA images, while segmented LA views allow more accurate estimation of the basal slice and can be used for slice registration. Since manual segmentation of CMR images is very tedious and time-consuming, its automation is highly required. In this paper, we propose a fully automatic 2D method for segmenting LV consecutively in LA and SA images. The approach was validated on 35 patients giving mean segmentation error smaller than one pixel, both for LA and SA, and accurate LV volume measurements.