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Uncertainty Quantification in Meningioma Segmentation using Bayesian Deep Learning
Aakanksha Rana · Patrick McClure · Omar Arnaout · Satra Ghosh

Quantifying confidence in predictions made by deep learning based segmentation systems is of utmost importance for clinical decision making as it can help quantify support for a decision. For Meningioma (a primary brain tumor) segmentation methods, uncertainty estimation is particularly interesting for efficient precision therapy, tumor-growth estimation, and patient-specific treatment planning. While models exists to estimate uncertainty for Glioma tissues, no study has been done to assess the confidence of a model's segmentation prediction for Meningiomas. In this paper, we train the first 3D Bayesian deep neural network (BNN) to segment Meningioma and simultaneously provide an uncertainty estimate. Using 10,674 MRI sequences (T1-w non-tumor and T1-w contrast-enhanced with tumor), we explore optimal training strategies and architectures for BNNs. We obtain a dice score of 0.828 on a held out dataset of 74 sequences. By predicting voxel-level uncertainty, we determine model's confidence in finding tumor regions with a precision which can further assist in downstream tasks such as radiation therapy planning. Our findings also serve as a proof-of-concept to access the quality of meningioma segmentations, which can potentially be used to flag outputs with high-errors that need further human review.

Author Information

Aakanksha Rana (MIT)
Patrick McClure (National Institutes of Health)
Omar Arnaout (BWH)
Satra Ghosh (MIT)

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