Shape quantization and recognition with randomized trees
Neural Computation
Information Retrieval
Machine Learning
Machine Learning
Applying improved fast marching method to endocardial boundary detection in echocardiographic images
Pattern Recognition Letters
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
An empirical comparison of supervised learning algorithms
ICML '06 Proceedings of the 23rd international conference on Machine learning
Design and Performance of a Fault-Tolerant Real-Time CORBA Event Service
ECRTS '06 Proceedings of the 18th Euromicro Conference on Real-Time Systems
Lesion detection using segmentation and classification of mammograms
AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications
Segmentation of SBFSEM Volume Data of Neural Tissue by Hierarchical Classification
Proceedings of the 30th DAGM symposium on Pattern Recognition
Intravascular ultrasound images vessel characterization using Adaboost
FIMH'03 Proceedings of the 2nd international conference on Functional imaging and modeling of the heart
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Spatial decision forests for MS lesion segmentation in multi-channel MR images
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
Recovering endocardial walls from 3D TEE
FIMH'11 Proceedings of the 6th international conference on Functional imaging and modeling of the heart
Entangled decision forests and their application for semantic segmentation of CT images
IPMI'11 Proceedings of the 22nd international conference on Information processing in medical imaging
MLMI'11 Proceedings of the Second international conference on Machine learning in medical imaging
MLMI'11 Proceedings of the Second international conference on Machine learning in medical imaging
Foundations and Trends® in Computer Graphics and Vision
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 hybrid segmentation of abdominal CT images
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
How many trees in a random forest?
MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
MCV'12 Proceedings of the Second international conference on Medical Computer Vision: recognition techniques and applications in medical imaging
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Automatic delineation of the myocardium in real-time 3D echocardiography may be used to aid the diagnosis of heart problems such as ischaemia, by enabling quantification of wall thickening and wall motion abnormalities. Distinguishing between myocardial and non-myocardial tissue is, however, difficult due to low signal-to-noise ratio as well as the efficiency constraints imposed on any algorithmic solution by the large size of the data under consideration. In this paper, we take a machine learning approach treating this problem as a two-class 3D patch classification task. We demonstrate that solving such task using random forests , which are the discriminative classifiers developed recently in the machine learning community, allows to obtain accurate delineations in a matter of seconds (on a CPU) or even in real-time (on a GPU) for the entire 3D volume.