Challenge on Endocardial Three-dimensional Ultrasound Segmentation
Issue

Title
Rating
Authors
Reviews
Status
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Clinical Expert Delineation of 3D Left Ventricular Echocardiograms for the CETUS Segmentation Challenge
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Papachristidis A., Geleijnse M.L., Galli E., Heyde B., Alessandrini M., Barbosa D., Monaghan M.J., Bernard O., D'hooge J., Bosch J.G.
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Open for public review
Segmentation of Multi-Center 3D Left Ventricular Echocardiograms by Active Appearance Models
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Van Stralen M., Haak A., Leung K.E., Van Burken G., Bosch J.G.
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Open for public review
Endocardial 3D Ultrasound Segmentation using Autocontext Random Forests
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Keraudren K., Oktay O., Shi W., Hajnal J.V., Rueckert D.
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Open for public review
Automatized Evaluation of the Left Ventricular Ejection Fraction from Echocardiographic Images Using Graph Cut
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Bernier M., Jodoin P., Lalande A.
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Open for public review
Left Ventricle Segmentation in Cardiac Ultrasound Using Hough-Forests With Implicit Shape and Appearance Priors
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Milletari F., Yigitsoy M., Navab N., Ahmadi S.
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Open for public review
Challenge on Endocardial Three-dimensional Ultrasound Segmentation (CETUS)
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Bernard O., Heyde B., Alessandrini M., Barbosa D., Camarasu-Pop S., Cervenansky F., Valette S., Mirea O., Galli E., Geleijnse M., Papachristidis A., Bosch J.G., D'hooge J.
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Open for public review
Real-time Tracking of the Left Ventricle in 3D Ultrasound Using Kalman Filter and Mean Value Coordinates
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Smistad E., Lindseth F.
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Open for public review
Learning Shape Representations for Multi-Atlas Endocardium Segmentation in 3D Echo Images
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Oktay O., Shi W., Keraudren K., Caballero J., Rueckert D.
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Open for public review
Endocardial Segmentation using Structured Random Forests in 3D Echocardiography
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Domingos J.S., Stebbing R.V., Noble A.J.
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Open for public review
Fast Tracking of the Left Ventricle Using Global Anatomical Affine Optical Flow and Local Recursive Block Matching
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Barbosa D., Friboulet D., D'hooge J., Bernard O.
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Open for public review
Model-based left ventricle segmentation in 3D ultrasound using phase image
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Wang C., Smedby O.
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Open for public review

The goal of this contest is to compare left ventricle segmentation methods for both End Diastolic and End Systolic phase instances. This will be done using a common database of 3D cardiac ultrasound images acquired from 45 patients and the associated manual references based on the analysis of three different experts.
Real-time 3D echocardiography has already been shown to be an accurate tool for left ventricular (LV) volume assessment. However, LV border identification remains a challenging task, mainly because of the low contrast of the images combined with drop-out artifacts and speckle. Image quality may differ significantly and image characteristics depend on ultrasound machine and operator settings.

There thus exist strong efforts in developing fully or semi-automatic algorithms able to capture the LV morphology. Many (semi-)automatic algorithms have been proposed for segmenting the LV border, but a systematic and fair comparison between such methods has so far been impossible due to lack of publicly available common datasets with expert-defined segmentations.

The aim of this challenge is thus to gather researchers around the field of LV segmentation in 3D cardiac ultrasound by providing such common dataset enabling direct algorithm comparison. Such platform will allow consistent evaluation of the proposed segmentation solutions while highlighting the best performing ones, thus contributing to a faster clinical translation of groundbreaking technical advances.

This issue was created the 10-13-2014, the paperdue date is 10-13-2014, the decision date is 10-13-2014, the publication date is 10-13-2014


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