Abstract
3D echocardiography is an imaging modality that enables a more complete and rapid cardiac function assessment. However, as a time-consuming procedure, it calls upon automatic view detection to enable fast 3D volume navigation and analysis. We propose a combinatorial model- and machine learning-based left ventricle (LV) apical view detection method consisting of three steps: first, multiscale local phase-based 3D boundary detection is used to fit a deformable model to the boundaries of the LV blood pool. After candidate slice extraction around the derived mid axis of the LV segmentation, we propose the use of local phase-based Fast Ray features to complement conventional Haar features in an AdaBoost-based framework for automated standardized LV apical view detection. Evaluation performed on a combination of healthy volunteers and clinical patients with different image quality and ultrasound probes show that apical plane views can be accurately identified in a 360 degree swipe of 3D frames.
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Acknowledgments
The authors are grateful for the financial support provided by the RCUK Centre for Doctoral Training in Healthcare Innovation (EP/G036861/1) and EPSRC grant EP/G030693/1. We would also like to thank Richard Stebbing, Kevin Smith, Carlos Arteta and Mohammad Yaqub for the helpful discussions and advice.
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Domingos, J.S., Lima, E., Leeson, P., Noble, J.A. (2014). Local Phase-Based Fast Ray Features for Automatic Left Ventricle Apical View Detection in 3D Echocardiography. In: Menze, B., Langs, G., Montillo, A., Kelm, M., Müller, H., Tu, Z. (eds) Medical Computer Vision. Large Data in Medical Imaging. MCV 2013. Lecture Notes in Computer Science(), vol 8331. Springer, Cham. https://doi.org/10.1007/978-3-319-05530-5_12
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DOI: https://doi.org/10.1007/978-3-319-05530-5_12
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