Year-end Sale % OFF 
Abstract
We propose a learning based approach to perform automatic segmentation of the left ventricle in 3D cardiac ultrasound images. The segmentation contour is estimated through the use of a variant of Hough forests whose object localization capabilities are coupled with a patch-wise, appearance driven, contour estimation strategy. The performance of the proposed method is evaluated on a dataset of 30 images acquired from 15 patients using different equipment and settings.
Highlights
Introduction and related workThe MICCAI 2014 Endocardial Three-Dimensional Ultrasound Segmentation Challenge provides the community with a challenging multi-site, multi-scanner echocardiography dataset of 45 subjects
We evaluated our algorithm on the MICCAI echocardiography segmentation challenge datasets
We performed two experiments, where in the first we perform a leave-one-out-cross validation on the training set, and in the second we do the training on the entire training set, and testing on the test volumes one-by-one
Summary
The MICCAI 2014 Endocardial Three-Dimensional Ultrasound Segmentation Challenge provides the community with a challenging multi-site, multi-scanner echocardiography dataset of 45 subjects. Their heart is scanned in end-diastolic (ED) and end-systolic (ES) phase instances, with the aim of automatic left ventricular (LV) volume assessment. The challenging nature of 3D realtime echocardiography images, which are affected by a large amount of noise and artefacts such as coarse speckle patterns and dropout regions, makes an automatic segmentation algorithm highly desirable. The localization of the left ventricle in the images is obtained in a fully automatic manner, using the Hough forest voting strategy. Each contribution to the contour is weighted considering local appearances
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
