TY - GEN
T1 - Towards a parts-based approach to sub-cortical brain structure parsing
AU - Gagneja, Digvijay
AU - Xiong, Caiming
AU - Corso, Jason J.
PY - 2011
Y1 - 2011
N2 - The automatic localization and segmentation, or parsing, of neuroanatomical brain structures is a key step in many neuroscience tasks. However, the inherent variability in these brain structures and their appearance continues to challenge medical image processing methods. The state of the art primarily relies upon local voxelbased morphometry, Markov random field, and probabilistic atlas based approaches, which limits the ability to explicitly capture the parts-based structure inherent in the brain. We propose a method that defines a principled parts-based representation of the sub-cortical brain structures. Our method is based on the pictorial structures model and jointly models the appearance of each part as well as the layout of the parts as a whole. Inference is cast as a maximum a posteriori problem and solved in a steepest-descent manner. Experimental results on a 28-case data set demonstrate high accuracy of our method and substantiate our claim that there is significant promise in a parts-based approach to modeling medical imaging structures.
AB - The automatic localization and segmentation, or parsing, of neuroanatomical brain structures is a key step in many neuroscience tasks. However, the inherent variability in these brain structures and their appearance continues to challenge medical image processing methods. The state of the art primarily relies upon local voxelbased morphometry, Markov random field, and probabilistic atlas based approaches, which limits the ability to explicitly capture the parts-based structure inherent in the brain. We propose a method that defines a principled parts-based representation of the sub-cortical brain structures. Our method is based on the pictorial structures model and jointly models the appearance of each part as well as the layout of the parts as a whole. Inference is cast as a maximum a posteriori problem and solved in a steepest-descent manner. Experimental results on a 28-case data set demonstrate high accuracy of our method and substantiate our claim that there is significant promise in a parts-based approach to modeling medical imaging structures.
KW - neuroimaging
KW - parts-based representation
KW - pictorial structures
KW - segmentation
KW - sub-cortical structures
UR - http://www.scopus.com/inward/record.url?scp=79958008975&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79958008975&partnerID=8YFLogxK
U2 - 10.1117/12.878170
DO - 10.1117/12.878170
M3 - Conference contribution
AN - SCOPUS:79958008975
SN - 9780819485045
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2011
T2 - Medical Imaging 2011: Image Processing
Y2 - 14 February 2011 through 16 February 2011
ER -