A voxel-wise uncertainty-guided framework for glioma segmentation using spherical projection-based U-Net and localized refinement.

ABSTRACT:

Background

Accurate segmentation of glioma subregions from multi-parametric MRI (MP-MRI) is critical for clinical management but remains challenging due to tumor heterogeneity and ambiguous tissue boundaries.

Purpose

This study proposes an uncertainty-guided hybrid segmentation framework that integrates spherical projection-based 2D modeling with localized 3D refinement to improve segmentation fidelity.

Methods

The framework was validated on the BraTS 2020 dataset (N = 369). First, a 2D nnU-Net with spherical projection deformation was employed to generate initial slice-wise predictions. Crucially, prediction variance across multiple spherical projections was utilized to quantify voxel-level uncertainty, highlighting regions of low model confidence. A kernel-based sli

SUBMITTER: Yang Z 

PROVIDER: S-EPMC12947053 | biostudies-literature | 2026 Mar

REPOSITORIES: biostudies-literature