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Abstract #2419

Do you Agree? An Exploration of Inter-rater Variability and Deep Learning Segmentation Uncertainty

Katharina Viktoria Hoebel1,2, Christopher P Bridge1,3, Jay Biren Patel1,2, Ken Chang1,2, Marco C Pinho1, Xiaoyue Ma4, Bruce R Rosen1, Tracy T Batchelor5, Elizabeth R Gerstner1,5, and Jayashree Kalpathy-Cramer1
1Athinoula A. Martinos Center for Biomedical Imaging, Boston, MA, United States, 2Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA, United States, 3MGH and BWH Center for Clinical Data Science, Boston, MA, United States, 4Department of Magnetic Resonance, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China, 5Stephen E. and Catherine Pappas Center for Neuro-Oncology, Massachusetts General Hospital, Boston, MA, United States

The outlines of target structures on medical imaging can be highly ambiguous. The uncertainty about the “true” outline is evident in high inter-rater variability of manual segmentations. So far, no method is available to identify cases likely to exhibit a high inter-rater variability. Here, we demonstrate that ground truth independent uncertainty metrics extracted from a MC dropout segmentation model developed on labels of only one rater correlate with inter-rater variability. This relationship can be used to identify ambiguous cases and flag them for more detailed review supporting consistent and reliable patient evaluation in research and clinical settings.

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