Anatomical labeling of the circle of willis using maximum a posteriori graph matching
A new method for anatomically labeling the vasculature is presented and applied to the Circle of Willis. Our method converts the segmented vasculature into a graph that is matched with an annotated graph atlas in a maximum a posteriori (MAP) way. The MAP matching is formulated as a quadratic binary...
Saved in:
| Published in: | Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Vol. 16; no. Pt 1; p. 566 |
|---|---|
| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
01.01.2013
|
| Online Access: | Get more information |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | A new method for anatomically labeling the vasculature is presented and applied to the Circle of Willis. Our method converts the segmented vasculature into a graph that is matched with an annotated graph atlas in a maximum a posteriori (MAP) way. The MAP matching is formulated as a quadratic binary programming problem which can be solved efficiently. Unlike previous methods, our approach can handle non tree-like vasculature and large topological differences. The method is evaluated in a leave-one-out test on MRA of 30 subjects where it achieves a sensitivity of 93% and a specificity of 85% with an average error of 1.5 mm on matching bifurcations in the vascular graph.A new method for anatomically labeling the vasculature is presented and applied to the Circle of Willis. Our method converts the segmented vasculature into a graph that is matched with an annotated graph atlas in a maximum a posteriori (MAP) way. The MAP matching is formulated as a quadratic binary programming problem which can be solved efficiently. Unlike previous methods, our approach can handle non tree-like vasculature and large topological differences. The method is evaluated in a leave-one-out test on MRA of 30 subjects where it achieves a sensitivity of 93% and a specificity of 85% with an average error of 1.5 mm on matching bifurcations in the vascular graph. |
|---|---|
| Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| DOI: | 10.1007/978-3-642-40811-3_71 |