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Abstract #0269

Efficient Anatomical Labeling by Statistical Recombination of Partially Label Datasets

Bennett Allan Landman1, John Anton Bogovic2, Jerry Ladd Prince2,3

1Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA; 2Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA; 3Biomedical Engineering, Johns Hopkins University, Balitmore, MD, USA

Manual labeling of medical imaging data is critical task for the assessment of volumetric and morphometric changes; however, even expert raters are imperfect and subject to variability. Existing techniques to combine data from multiple raters require that each rater generate a complete dataset. We propose a robust extension which allows for missing data, accounts for repeated tasks, and utilizes training data. With our technique, numerous raters can label small, overlapping portions of a large dataset, and rater heterogeneity can be robustly controlled while estimating a single, reliable label set. This enables parallel processing and reduces detrimental impacts of rater unavailability.