3D Active shape models use a set of annotated volumes to learn a shape model. The shape model is defined by a fixed number of landmarks at specific locations and takes shape constraints into account in the segmentation process. A relevant problem in which these models can be used is the segmentation of the left ventricle in 3D MRI volumes. In this problem, the annotations correspond to a set of contours that define the LV border at each volume slice. However, each volume has a different number of slices (i.e., a different number of landmarks), which makes model learning difficult. Furthermore, motion artifacts and the large distance between slices make interpolation of voxel intensities a bad choice when applying the learned model to a test volume. These two problems raise the following questions: (1) how can we learn a shape model from volumes with a variable number of slices? and (2) how can we segment a test volume without interpolating voxel intensities between slices? This paper provides an answer to these questions and proposes a 3D active shape model that can be used to segment the left ventricle in cardiac MRI.
CITATION STYLE
Santiago, C., Nascimento, J. C., & Marques, J. S. (2015). Robust 3D active shape model for the segmentation of the left ventricle in MRI. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9117, pp. 283–290). Springer Verlag. https://doi.org/10.1007/978-3-319-19390-8_32
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