Towards a parts-based approach to sub-cortical brain structure parsing
Digvijay Gagneja, Caiming Xiong, Jason J. Corso · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2011
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.