Automatic LV Feature Detection and Blood-Pool Tracking from Multi-plane TEE Time Series
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.
KeywordsLeft Ventricle Target Registration Error Volume Reconstruction Left Ventricle Volume Active Contour Method
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.
- 8.Georgescu, B., Zhou, X., Comaniciu, D., Gupta, A.: Database-guided segmentation of anatomical structures with complex appearance. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 2, pp. 429–436 (2005)Google Scholar
- 12.Kovesi, P.: Symmetry and asymmetry from local phase. In: Proceedings of 10th Australian Joint Conference Artificial Intelligence, pp. 2–4 (1997)Google Scholar
- 13.Rajpoot, K., Grau, V., Noble, J.A.: Local-phase based 3D boundary detection using monogenic signal and its application to real-time 3-D echocardiography images. In: Proceedings of IEEE International Symposium Biomedical Imaging, pp. 783–786 (2009)Google Scholar
- 14.Uzkent, B., Hoffman, M.J., Cherry, E., Cahill, N.: Processing IEEE western NY image signal process workshop, pp. 47–51 (2014)Google Scholar