A hierarchical algorithm for MR brain image parcellation.
We introduce an algorithm for segmenting brain magnetic resonance (MR) images into anatomical compartments such as the major tissue classes and neuro-anatomical structures of the gray matter. The algorithm is guided by prior information represented within a tree structure. The tree mirrors the hierarchy of anatomical structures and the subtrees correspond to limited segmentation problems. The solution to each problem is estimated via a conventional classifier. Our algorithm can be adapted to a wide range of segmentation problems by modifying the tree structure or replacing the classifier. We evaluate the performance of our new segmentation approach by revisiting a previously published statistical group comparison between first-episode schizophrenia patients, first-episode affective psychosis patients, and comparison subjects. The original study is based on 50 MR volumes in which an expert identified the brain tissue classes as well as the superior temporal gyrus, amygdala, and hippocampus. We generate analogous segmentations using our new method and repeat the statistical group comparison. The results of our analysis are similar to the original findings, except for one structure (the left superior temporal gyrus) in which a trend-level statistical significance (p = 0.07) was observed instead of statistical significance.
Pohl
Kilian M
KM
Surgical Planning Laboratory, Harvard Medical School and Brigham and Women's Hospital, Boston, MA 02115, USA. pohl@csail.mit.edu
Bouix
Sylvain
S
Nakamura
Motoaki
M
Rohlfing
Torsten
T
McCarley
Robert W
RW
Kikinis
Ron
R
Grimson
W Eric L
WE
Shenton
Martha E
ME
Wells
William M
WM
D000341:Affective Disorders, Psychotic / Q000175:diagnosis*; D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000473:pathology*; D003198:Computer Simulation; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008959:Models, Neurological; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012559:Schizophrenia / Q000175:diagnosis*; D012680:Sensitivity and Specificity