Abstract
Multi-plane, 2D TEE images constitute the clinical standard of care for assessment of left ventricle function, as well as for guiding various minimally invasive procedure that rely on intra-operative imaging for real-time visualization. We propose a framework that enables automatic, rapid and accurate endocardial left ventricle feature identification and blood-pool segmentation using a combination of image filtering, graph cut, non-rigid registration-based motion extraction, and 3D LV geometry reconstruction techniques applied to the TEE image series. We evaluate our proposed framework using several retrospective patient tri-plane TEE image sequences and demonstrate comparable results to those achieved by expert manual segmentation using clinical software.
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Acknowledgments
The authors would like to acknowledge Dr. Nathan Cahill for sharing his technical expertise and Aditya Daryanani for his help with image segmentation. In addition, we acknowledge funding support from the Kate Gleason Research Fund and the RIT College of Engineering Faculty Development Grant.
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Dangi, S., Ben-Zikri, Y.K., Lamash, Y., Schwarz, K.Q., Linte, C.A. (2015). Automatic LV Feature Detection and Blood-Pool Tracking from Multi-plane TEE Time Series. In: van Assen, H., Bovendeerd, P., Delhaas, T. (eds) Functional Imaging and Modeling of the Heart. FIMH 2015. Lecture Notes in Computer Science(), vol 9126. Springer, Cham. https://doi.org/10.1007/978-3-319-20309-6_4
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DOI: https://doi.org/10.1007/978-3-319-20309-6_4
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