title: Task-optimal registration cost functions.
abstract: In this paper, we propose a framework for learning the parameters of registration cost functions--such as the tradeoff between the regularization and image similiarity term--with respect to a specific task. Assuming the existence of labeled training data, we specialize the framework for the task of localizing hidden labels via image registration. We learn the parameters of the weighted sum of squared differences (wSSD) image similarity term that are optimal for the localization of Brodmann areas (BAs) in a new subject based on cortical geometry. We demonstrate state-of-the-art localization of V1, V2, BA44 and BA45.
authors.affiliation: Computer Science and Artificial Intelligence Laboratory, MIT, USA. ythomas@csail.mit.edu
authors.forename: B T Thomas
authors.initials: BT
authors.lastname: Yeo
authors.forename: Mert
authors.initials: M
authors.lastname: Sabuncu
authors.forename: Polina
authors.initials: P
authors.lastname: Golland
authors.forename: Bruce
authors.initials: B
authors.lastname: Fischl
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique