Automatic Mitral Valve Inflow Measurements from Doppler Echocardiography
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Discriminative Learning for Deformable Shape Segmentation: A Comparative Study
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
AutoGate: Fast and Automatic Doppler Gate Localization in B-Mode Echocardiogram
MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
FIMH '09 Proceedings of the 5th International Conference on Functional Imaging and Modeling of the Heart
FIMH '09 Proceedings of the 5th International Conference on Functional Imaging and Modeling of the Heart
Learning sparse kernels from 3D surfaces for heart wall motion abnormality detection
IAAI'08 Proceedings of the 20th national conference on Innovative applications of artificial intelligence - Volume 3
Automated heart wall motion abnormality detection from ultrasound images using Bayesian networks
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Proceedings of the international conference on Multimedia information retrieval
Automatic fetal measurements in ultrasound using constrained probabilistic boosting tree
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
Context ranking machine and its application to rigid localization of deformable objects
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Detection and retrieval of cysts in joint ultrasound B-mode and elasticity breast images
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Robust ultrasound image analysis using learning
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Database guided detection of anatomical landmark points in 3D images of the heart
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Cardiac anchoring in MRI through context modeling
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
Detection of 3D spinal geometry using iterated marginal space learning
MCV'10 Proceedings of the 2010 international MICCAI conference on Medical computer vision: recognition techniques and applications in medical imaging
Database-guided simultaneous multi-slice 3d segmentation for volumetric data
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Example based non-rigid shape detection
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Landmark detection in cardiac MRI using learned local image statistics
STACOM'12 Proceedings of the third international conference on Statistical Atlases and Computational Models of the Heart: imaging and modelling challenges
Extraction of left ventricle borders with local and global priors from echocardiograms
Machine Vision and Applications
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The segmentation of anatomical structures has been traditionally formulated as a perceptual grouping task, and solved through clustering and variational approaches. However, such strategies require the a priori knowledge to be explicitly defined in the optimization criterion, e.g., "high-gradient border", "smoothness", or "similar intensity or texture". This approach is limited by the validity of underlying assumptions and cannot capture complex structure appearance. This paper introduces database-guided segmentation as a new data-driven paradigm that directly exploits expert annotation of interest structures in large medical databases. Segmentation is formulated as a two-step learning problem. The first step is structure detection where we learn how to discriminate between the object of interest and background. The resulting classifier based on a boosted cascade of simple features also provides a global rigid transformation of the structure. The second step is shape inference where we use a sample-based representation of the joint distribution of appearance and shape annotations. To learn the association between the complex appearance and shape we propose a feature selection mechanism and the corresponding metric. We show that the selected features are better than using directly the appearance and illustrate the performance of the proposed method on a large set of ultrasound heart images.