TY - GEN
T1 - 3D segmentation of rodent brains using deformable models and variational methods
AU - Zhang, Shaoting
AU - Zhou, Jinghao
AU - Wang, Xiaoxu
AU - Chang, Sukmoon
AU - Metaxas, Dimitris N.
AU - Pappas, George
AU - Delis, Foteini
AU - Volkow, Nora D.
AU - Wang, Gene Jack
AU - Thanos, Panayotis K.
AU - Kambhamettu, Chandra
PY - 2009
Y1 - 2009
N2 - 3D functional segmentation of brain images is important in understating the relationships between anatomy and mental diseases in brains.tric analysis of various brain structures such as the cerebellum plays a critical role in studying the structural changes in brain regions as a function of development, trauma, or neurodegeneratioin. Although various segmentation methods in clinical studies have been proposed, many of them require a priori knowledge about the locations of the structures of interest, which prevents the fully automatic segmentation. Besides, the topological changes of structures are difficult to detect. In this paper, we present a novel method for detecting and locating the brain structures of interest that can be used for the fully automatic 3D functional segmentation of rodent brain MR images. The presented method is based on active shape model (ASM), Metamorph models and variational techniques. It focuses on detecting the topological changes of brain structures based on a novel ratio criteria. The mean successful rate of the topological change detection shows 86.6% accuracy compared to the expert identified ground truth.
AB - 3D functional segmentation of brain images is important in understating the relationships between anatomy and mental diseases in brains.tric analysis of various brain structures such as the cerebellum plays a critical role in studying the structural changes in brain regions as a function of development, trauma, or neurodegeneratioin. Although various segmentation methods in clinical studies have been proposed, many of them require a priori knowledge about the locations of the structures of interest, which prevents the fully automatic segmentation. Besides, the topological changes of structures are difficult to detect. In this paper, we present a novel method for detecting and locating the brain structures of interest that can be used for the fully automatic 3D functional segmentation of rodent brain MR images. The presented method is based on active shape model (ASM), Metamorph models and variational techniques. It focuses on detecting the topological changes of brain structures based on a novel ratio criteria. The mean successful rate of the topological change detection shows 86.6% accuracy compared to the expert identified ground truth.
UR - http://www.scopus.com/inward/record.url?scp=70449572264&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=70449572264&partnerID=8YFLogxK
U2 - 10.1109/CVPR.2009.5204051
DO - 10.1109/CVPR.2009.5204051
M3 - Conference contribution
AN - SCOPUS:70449572264
SN - 9781424439911
T3 - 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009
SP - 94
EP - 100
BT - 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009
PB - IEEE Computer Society
T2 - 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009
Y2 - 20 June 2009 through 25 June 2009
ER -