Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Computer-assisted endocardial border identification from a sequence of two-dimensional echocardiographic images
Convex Optimization
Database-Guided Segmentation of Anatomical Structures with Complex Appearance
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
Robust Shape Tracking With Multiple Models in Ultrasound Images
IEEE Transactions on Image Processing
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The automatic segmentation of the left ventricle of the heart in ultrasound images has been a core research topic in medical Image analysis. Most of the solutions are based on low-level segmentation methods, which uses a prior model of the appearance of the left ventricle, but imaging conditions violating the assumptions present in the prior can damage their performance. Recently, pattern recognition methods have become more robust to imaging conditions by automatically building an appearance model from training images, but they present a few challenges, such as: the need of a large set of training Images, robustness to imaging conditions not present in the training data, and complex search process. In this paper we handle the second problem using the recently proposed deep neural network and the third problem with efficient searching algorithms. Quantitative comparisons show that the accuracy of our approach is higher than state-of-the-art methods. The results also show that efficient search strategies reduce ten times the run-time complexity.