Joint Bayesian cortical sulci recognition and spatial normalization.
In this paper, we study the recognition of about 60 sulcal structures over a new T1 MRI database of 62 subjects. It continues our previous work [7] and more specifically extends the localization model of sulci (SPAM). This model is sensitive to the chosen common space during the group study. Thus, we focus the current work on refining this space using registration techniques. Nevertheless, we also benefit from the sulcuswise localization variability knowledge to constrain the normalization. So, we propose a consistent Bayesian framework to jointly identify and register sulci, with two complementary normalization techniques and their detailed integration in the model: a global rigid transformation followed by a piecewise rigid-one, sulcus after sulcus. Thereby, we have improved the sulci labeling quality to a global recognition rate of 86%, and moreover obtained a basic but robust registration technique.
Perrot
Matthieu
M
CEA, Neurospin, LNAO, Saclay, France. matthieu.perrot@cea.fr
Rivière
Denis
D
Tucholka
Alan
A
Mangin
Jean-François
JF
D000465:Algorithms; D001499:Bayes Theorem; D002540:Cerebral Cortex / Q000033:anatomy & histology*; D003198:Computer Simulation; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008953:Models, Anatomic; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique