Combining Multiple Dynamic Models and Deep Learning Architectures for Tracking the Left Ventricle Endocardium in Ultrasound Data
Issue No. 11 - Nov. (2013 vol. 35)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPAMI.2013.96
G. Carneiro , Australian Centre for Visual Technol., Univ. of Adelaide, Adelaide, SA, Australia
J. C. Nascimento , Inst. de Sist. e Robot., Inst. Super. Tecnico, Lisbon, Portugal
We present a new statistical pattern recognition approach for the problem of left ventricle endocardium tracking in ultrasound data. The problem is formulated as a sequential importance resampling algorithm such that the expected segmentation of the current time step is estimated based on the appearance, shape, and motion models that take into account all previous and current images and previous segmentation contours produced by the method. The new appearance and shape models decouple the affine and nonrigid segmentations of the left ventricle to reduce the running time complexity. The proposed motion model combines the systole and diastole motion patterns and an observation distribution built by a deep neural network. The functionality of our approach is evaluated using a dataset of diseased cases containing 16 sequences and another dataset of normal cases comprised of four sequences, where both sets present long axis views of the left ventricle. Using a training set comprised of diseased and healthy cases, we show that our approach produces more accurate results than current state-of-the-art endocardium tracking methods in two test sequences from healthy subjects. Using three test sequences containing different types of cardiopathies, we show that our method correlates well with interuser statistics produced by four cardiologists.
Shape, Motion segmentation, Image segmentation, Training, Computational modeling, Tracking, Imaging
G. Carneiro, J. C. Nascimento, "Combining Multiple Dynamic Models and Deep Learning Architectures for Tracking the Left Ventricle Endocardium in Ultrasound Data", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 35, no. , pp. 2592-2607, Nov. 2013, doi:10.1109/TPAMI.2013.96