An algorithm for optimal fusion of atlases with different labeling protocols

Research output: Contribution to journalArticleScientificpeer-review

Researchers

  • Juan Eugenio Iglesias
  • Mert Rory Sabuncu
  • Iman Aganj
  • Priyanka Bhatt
  • Christen Casillas
  • David Salat
  • Adam Boxer
  • Bruce Fischl
  • Koen Van Leemput

Research units

  • University of California at San Francisco
  • Danmarks Tekniske Universitet
  • Massachusetts Institute of Technology
  • Harvard University
  • Basque Center on Cognition, Brain and Language

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.

Details

Original languageEnglish
Pages (from-to)451-463
Number of pages13
JournalNeuroImage
Volume106
Publication statusPublished - 1 Feb 2015
MoE publication typeA1 Journal article-refereed

    Research areas

  • Label fusion, Segmentation

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