title: An algorithm for optimal fusion of atlases with different labeling protocols.
abstract: In this paper we present a novel label fusion algorithm suited for scenarios in which different manual delineation protocols with potentially disparate structures have been used to annotate the training scans (hereafter referred to as "atlases"). Such scenarios arise when atlases have missing structures, when they have been labeled with different levels of detail, or when they have been taken from different heterogeneous databases. The proposed algorithm can be used to automatically label a novel scan with any of the protocols from the training data. Further, it enables us to generate new labels that are not present in any delineation protocol by defining intersections on the underling labels. We first use probabilistic models of label fusion to generalize three popular label fusion techniques to the multi-protocol setting: majority voting, semi-locally weighted voting and STAPLE. Then, we identify some shortcomings of the generalized methods, namely the inability to produce meaningful posterior probabilities for the different labels (majority voting, semi-locally weighted voting) and to exploit the similarities between the atlases (all three methods). Finally, we propose a novel generative label fusion model that can overcome these drawbacks. We use the proposed method to combine four brain MRI datasets labeled with different protocols (with a total of 102 unique labeled structures) to produce segmentations of 148 brain regions. Using cross-validation, we show that the proposed algorithm outperforms the generalizations of majority voting, semi-locally weighted voting and STAPLE (mean Dice score 83%, vs. 77%, 80% and 79%, respectively). We also evaluated the proposed algorithm in an aging study, successfully reproducing some well-known results in cortical and subcortical structures.
authors.affiliation: Basque Center on Cognition, Brain and Language (BCBL), Spain. Electronic address: e.iglesias@bcbl.eu.
authors.forename: Juan Eugenio
authors.initials: JE
authors.lastname: Iglesias
authors.affiliation: Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School/Massachusetts General Hospital, Charlestown, MA, USA; MIT Computer Science and Artificial Intelligence Laboratory (CSAIL), USA.
authors.forename: Mert Rory
authors.initials: MR
authors.lastname: Sabuncu
authors.affiliation: Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School/Massachusetts General Hospital, Charlestown, MA, USA.
authors.forename: Iman
authors.initials: I
authors.lastname: Aganj
authors.affiliation: Memory and Aging Center, University of California, San Francisco, USA.
authors.forename: Priyanka
authors.initials: P
authors.lastname: Bhatt
authors.affiliation: Memory and Aging Center, University of California, San Francisco, USA.
authors.forename: Christen
authors.initials: C
authors.lastname: Casillas
authors.affiliation: Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School/Massachusetts General Hospital, Charlestown, MA, USA.
authors.forename: David
authors.initials: D
authors.lastname: Salat
authors.affiliation: Memory and Aging Center, University of California, San Francisco, USA.
authors.forename: Adam
authors.initials: A
authors.lastname: Boxer
authors.affiliation: MIT Computer Science and Artificial Intelligence Laboratory (CSAIL), USA; Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School/Massachusetts General Hospital, Charlestown, MA, USA.
authors.forename: Bruce
authors.initials: B
authors.lastname: Fischl
authors.affiliation: Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School/Massachusetts General Hospital, Charlestown, MA, USA; Department of Applied Mathematics and Computer Science, Technical University of Denmark, Denmark; Department of Information and Computer Science, Aalto University, Finland; Department of Biomedical Engineering and Computational Science, Aalto University, Finland.
authors.forename: Koen
authors.initials: K
authors.lastname: Van Leemput
mesh_terms: D000375:Aging; D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology* / Q000502:physiology; D016208:Databases, Factual; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008875:Middle Aged; D008959:Models, Neurological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results
keywords: Label fusion; Segmentation