标题： 基于球面投影的U-Net和多参数MRI中局部优化的体素级不确定性引导框架用于胶质瘤分割 显示英文标题

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

摘要： 目的：多参数磁共振成像（MP-MRI）中胶质瘤亚区域的准确分割对于诊断和治疗计划至关重要，但由于肿瘤异质性和模糊边界而仍然具有挑战性。本研究提出了一种基于不确定性的混合框架，将基于球面投影的2D建模与目标3D细化相结合，以提高分割精度和可解释性。 方法：使用BraTS2020数据集（369名患者，四种模态的MP-MRI），训练了三个2D U-Net来分割增强肿瘤（ET）、肿瘤核心（TC）和整个肿瘤（WT）。通过基于球面投影的2D nnU-Net量化体素级不确定性，捕捉变形输入中的预测方差。使用3D滑动窗口识别高不确定性区域，并使用专用的3D nnU-Net进行细化。最终输出通过加权融合结合2D和3D预测，通过粒子群优化进行优化。 结果：所提出的方法优于独立的2D和3D基线，Dice分数分别为0.8124（ET）、0.7499（TC）和0.9055（WT），在敏感性和视觉一致性方面均有稳定提升。 结论：本研究提出了一种新颖的不确定性感知分割策略，自适应地整合2D和3D建模。通过专注于模糊区域的细化，它提高了效率和准确性，为精准神经肿瘤学和其他高风险医学影像任务提供了广泛的应用前景。 显示英文摘要

摘要： 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.