Crohn's Disease Segmentation from MRI Using Learned Image Priors. Mahapatra, D.; Schüffler, P. J.; Vos, F. M.; and Buhmann, J. M. In Proceedings IEEE ISBI 2015, pages 625–628, 2015.
Crohn's Disease Segmentation from MRI Using Learned Image Priors [link]Paper  abstract   bibtex   
We use a Field of Experts (FoE) model to segment abdominal regions from MRI affected with Crohns Disease (CD). FoE learns a prior model of diseased and normal bowel, and background non-bowel tissues from manually annotated training images. Unlike current approaches, FoE does not rely on hand designed features but learns the most discriminative features (in the form of filters) for different classes. FoE filter responses are integrated into a Random forest (RF) model that outputs probability maps for the test image and finally segments the diseased region. Experimental results show our method achieves significantly better performance than existing methods.
@inproceedings{mahapatra_crohns_2015,
	title = {Crohn's {Disease} {Segmentation} from {MRI} {Using} {Learned} {Image} {Priors}},
	url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7163951},
	abstract = {We use a Field of Experts (FoE) model to segment abdominal regions from MRI affected with Crohns Disease (CD). FoE learns a prior model of diseased and normal bowel, and background non-bowel tissues from manually annotated training images. Unlike current approaches, FoE does not rely on hand designed features but learns the most discriminative features (in the form of filters) for different classes. FoE filter responses are integrated into a Random forest (RF) model that outputs probability maps for the test image and finally segments the diseased region. Experimental results show our method achieves significantly better performance than existing methods.},
	booktitle = {Proceedings {IEEE} {ISBI} 2015},
	author = {Mahapatra, D. and Schüffler, P. J. and Vos, F. M. and Buhmann, J. M.},
	year = {2015},
	pages = {625--628},
}
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