Uncertainty Evaluation in Deep Learning Brain Tumor Segmentation

Abstract

Meningiomas are the most common primary brain tumors and often times treated throughradiation therapy. With the surge of interest in deep neural network (DNN) model applications, image segmentation of meningioma radiotherapy target, gross tumor volume (GTV), can improve clinical outcomes and clinic efficiency. A major limitation of DNN applications is the lack of a standardized methodology for quantification of uncertainty. DNNs are prone to making unexpected errors, hindering its safe transition into clinical applications. This work investigates the use of a spherical projection-based U-Net (SPU-Net) segmentation model to improve meningioma segmentation performance and allow for uncertainty quantification. As an equivalence of nonlinear image transform, spherical projection enhances locoregional details while maintaining the global field of view. By employing multiple projection centers, SPU-Net generates various GTV segmentation predictions, with the variance indicating the model's uncertainty. This uncertainty is quantified on a pixel-wise basis using entropy calculations and aggregated through Otsu’s method for final segmentation. The SPU-Net model surpassed the traditional U-Net in sensitivity (0.758 vs. 0.746), Dice coefficient (0.760 vs. 0.742), and reduced Hausdorff distance (2.682 cm vs 2.912 cm). Uncertainty mapping revealed low uncertainty in accurate segments (e.g., within GTV or healthy tissue) and higher uncertainty in problematic areas (e.g., GTV boundaries, dural tail), providing valuable insights for potential manual corrections. SPU-Net enhances the performance of MR-based meningioma GTV segmentation and provides uncertainty quantification. This advancement is particularly valuable given the complex extra-axial nature of meningiomas and their involvement with dural tissue, offering a significant improvement over traditional segmentation approaches.