A Voxel-Wise Uncertainty-Guided Framework for Glioma Segmentation Using Spherical Projection-Based U-Net and Localized Refinement in Multi-Parametric MRI

TL;DR

This paper introduces an uncertainty-guided hybrid framework combining spherical projection-based 2D modeling with localized 3D refinement to improve glioma segmentation accuracy in multi-parametric MRI, enhancing interpretability and efficiency.

Contribution

It presents a novel uncertainty-aware segmentation method that adaptively integrates 2D spherical projection and 3D refinement, outperforming traditional standalone models.

Findings

Achieved Dice scores of 0.8124 (ET), 0.7499 (TC), 0.9055 (WT).

Improved sensitivity and visual coherence over baseline models.

Focused refinement on high-uncertainty regions enhances accuracy.

Abstract

Purpose: Accurate segmentation of glioma subregions in multi-parametric MRI (MP-MRI) is essential for diagnosis and treatment planning but remains challenging due to tumor heterogeneity and ambiguous boundaries. This study proposes an uncertainty-guided hybrid framework integrating spherical projection-based 2D modeling with targeted 3D refinement to enhance segmentation accuracy and interpretability. Methods: Using the BraTS2020 dataset (369 patients, four-modality MP-MRI), three 2D U-Nets were trained to segment enhancing tumor (ET), tumor core (TC), and whole tumor (WT). Voxel-wise uncertainty was quantified via a spherical projection-based 2D nnU-Net, capturing prediction variance across deformed inputs. A 3D sliding window was used to identify high-uncertainty regions, which were refined using a dedicated 3D nnU-Net. Final outputs combined 2D and 3D predictions through a weighted fusion optimized via Particle Swarm Optimization. Results: The proposed method outperformed standalone 2D and 3D baselines, achieving Dice scores of 0.8124 (ET), 0.7499 (TC), and 0.9055 (WT), with consistent gains in sensitivity and visual coherence. Conclusion: This work presents a novel uncertainty-aware segmentation strategy that adaptively integrates 2D and 3D modeling. By focusing refinement on ambiguous regions, it improves both efficiency and accuracy, offering broad applicability to precision neuro-oncology and other high-stakes medical imaging tasks.