Abstract #2015

Consolidation of expert ratings of motion artifacts using hierarchical label fusion

Yael Balbastre^1,2, Robert Frost^1,2, Khushi Morparia^1,3, Richard L Carrington III¹, Yuh-Shin Chang^1,4,5, Brooks P Applewhite^1,4, Marcio Aloisio Bezerra Cavalcanti Rockenbach⁶, and Bruce Fischl^1,2,7,8

¹Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, United States, ²Department of Radiology, Harvard Medical School, Boston, MA, United States, ³Northeastern University, Boston, MA, United States, ⁴Department of Radiology, Massachusetts General Hospital, Boston, MA, United States, ⁵Department of Radiology, Massachusetts Eye and Ear Infirmary, Boston, MA, United States, ⁶Data Science Office, Mass General Brigham, Somerville, MA, United States, ⁷Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology,, Cambridge, MA, United States, ⁸Harvard-MIT Divison of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States

Synopsis

Keywords: Data Analysis, Motion CorrectionIntra-scan motion costs tens of thousands of dollars per scanner annually due to the need to repeat non-diagnostic scans¹. When assessing the scale of the problem and potential solutions, radiologists’ ratings of artifacts are considered the gold standard. However, inconsistent and conflicting ratings must be consolidated into a single gold-standard. We introduce a hierarchical label fusion algorithm that infers each rater's performance and promotes consistency across slices from a volume. This algorithm reduces label noise compared to majority votes, and allows non-expert ratings to be calibrated and included as additional silver-standards.

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