Graph cut segmentation with a statistical shape model in cardiac MRI

Graph cut segmentation with a statistical shape model in cardiac MRI. Grosgeorge, D., Petitjean, C., Dacher, J., & Ruan, S. Computer Vision and Image Understanding, 117(9):1027–1035, September, 2013.

Paper doi abstract bibtex

Segmenting the right ventricle (RV) in magnetic resonance (MR) images is required for cardiac function assessment. The segmentation of the RV is a difﬁcult task due to low contrast with surrounding tissues and high shape variability. To overcome these problems, we introduce a segmentation method based on a statistical shape model obtained with a principal component analysis (PCA) on a set of representative shapes of the RV. Shapes are not represented by a set of points, but by distance maps to their contour, relaxing the need for a costly landmark detection and matching process. A shape model is thus obtained by computing a PCA on the shape variations. This prior is registered onto the image via a very simple user interaction and then incorporated into the well-known graph cut framework in order to guide the segmentation. Our semi-automatic segmentation method has been applied on 248 MR images of a publicly available dataset (from MICCAI’12 Right Ventricle Segmentation Challenge). We show that encouraging results can be obtained for this challenging application.

@article{grosgeorge_graph_2013,
	title = {Graph cut segmentation with a statistical shape model in cardiac {MRI}},
	volume = {117},
	issn = {10773142},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S1077314213000738},
	doi = {10.1016/j.cviu.2013.01.014},
	abstract = {Segmenting the right ventricle (RV) in magnetic resonance (MR) images is required for cardiac function assessment. The segmentation of the RV is a difﬁcult task due to low contrast with surrounding tissues and high shape variability. To overcome these problems, we introduce a segmentation method based on a statistical shape model obtained with a principal component analysis (PCA) on a set of representative shapes of the RV. Shapes are not represented by a set of points, but by distance maps to their contour, relaxing the need for a costly landmark detection and matching process. A shape model is thus obtained by computing a PCA on the shape variations. This prior is registered onto the image via a very simple user interaction and then incorporated into the well-known graph cut framework in order to guide the segmentation. Our semi-automatic segmentation method has been applied on 248 MR images of a publicly available dataset (from MICCAI’12 Right Ventricle Segmentation Challenge). We show that encouraging results can be obtained for this challenging application.},
	language = {en},
	number = {9},
	urldate = {2021-02-12},
	journal = {Computer Vision and Image Understanding},
	author = {Grosgeorge, D. and Petitjean, C. and Dacher, J.-N. and Ruan, S.},
	month = sep,
	year = {2013},
	pages = {1027--1035},
}

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