Automatic segmentation of echocardiographic sequences by active appearance motion models

By J. Bosch, Steven C. Mitchell, Boudewijn P. F. Lelieveldt, F. Nijland, O. Kamp, Milan Sonka, Johan Hans C. Reiber. IEEE Transactions on Medical Imaging, 2002

340 citations recorded by the source corpus.

Subfields: Cardiology and Cardiovascular Medicine, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/8fb0252bd543dff85f885b7fd989a4b763e94fb7

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In Cardiology and Cardiovascular Medicine

In Computer Vision and Pattern Recognition

Citations in the corpus

Efficient and generalizable statistical models of shape and appearance for analysis of cardiac MRI (2008, background): “There are various reasons why the algorithms described in the literature for the segmentation and functional analysis of cardiac images exhibit lower success rates than that of human experts (Bosch et al., 2002; Gering, 2003; Mitchell et al., 2001, 2002):”
Ultrasound image segmentation: a survey (2006, methodology): “The method was evaluated on the same data as used in [13].”
Segmentation of 4D Cardiac MR Images Using a Probabilistic Atlas and the EM Algorithm (2003, background): “, 2002) and time sequences (Bosch et al., 2002) have been recently proposed.”
3-D active appearance models: segmentation of cardiac MR and ultrasound images (2002, methodology): “Data: To allow comparison of 3-D AAM results with the 2-D time AAM approach, the method was tested on the same data set [ 16 ].”
Spatio-temporal nonrigid registration for ultrasound cardiac motion estimation (2005, methodology): “Some of these methods try to overcome the intrinsic complexity of the data by introducing a priori knowledge by modelling a statistical representation of the possible motions and shapes [24, 25].”
Fully Automated Echocardiogram Interpretation in Clinical Practice (2018, methodology): “In terms of segmentation, there are several examples of automated approaches to segmenting the LV, including the use of active appearance models 24 and a more recent example using deep learning.”
Statistical shape models for 3D medical image segmentation: A review (2009, background): “Nonetheless, complete region-based appearance models can be built for these cases by reducing the resolution of the employed texture, as several studies demonstrate (Beichel et al., 2002; Bosch et al., 2002; Mitchell et al., 2002).”