Accurate brain tumor segmentation from multiparametric magnetic resonance imaging (MRI) is critical for treatment planning, response assessment, and quantitative neuro-oncology research. However, automated segmentation remains a difficult task in computer vision because of variation in tumor appearance and MRI protocols across patient scans. Moreover, clinically important regions such as enhancing tumor (ET) and tumor core (TC) are often small relative to the full brain volume, furthering increasing the difficulty of achieving high voxel-level precision. In this paper, we show that combining a modern 3D convolutional segmentation model with corrective diffusion-based refinement and ensembling improves volumetric glioma segmentation on the UTSW-Glioma dataset. We propose CoMNeT, a MedNeXt-CorrDiff framework that uses four MRI modalities as input and predicts ET, TC, and whole tumor (WT) regions for automated brain tumor segmentation. MedNeXt is used as the primary segmentation model with Global Response Normalization for feature learning, while CorrDiff is trained as a postprocessing residual refinement method to correct errors in the probability maps before final thresholding. Using five-fold cross-validation, CoMNeT achieved the highest Dice score for most tumor regions, with ET, TC, WT, and average Dice scores of 0.7543 +/- 0.0261, 0.6806 +/- 0.0166, 0.9049 +/- 0.0128, and 0.7798 +/- 0.0184, respectively. CoMNeT outperformed two selected baseline models: SegResNet (0.7555 +/- 0.0190 average Dice) and standalone MedNeXt (0.7697 +/- 0.0154 average Dice). Our findings support the use of corrective diffusion and fold-level probability ensembling as practical additions to existing state-of-the-art 3D convolutional models for automated glioma segmentation.

翻译：从多参数磁共振成像（MRI）中准确分割脑肿瘤对于治疗规划、疗效评估及定量神经肿瘤学研究至关重要。然而，由于不同患者扫描中肿瘤外观和MRI方案的差异，自动分割仍是计算机视觉领域的一项艰巨任务。此外，临床上重要的区域（如增强肿瘤（ET）和肿瘤核心（TC））通常相对于全脑体积较小，进一步增加了实现体素级高精度分割的难度。本文证明，将现代3D卷积分割模型与基于修正扩散的细化及集成方法相结合，能够提升在UTSW-Glioma数据集上的胶质瘤体积分割性能。我们提出CoMNeT框架，该框架基于MedNeXt-CorrDiff架构，以四种MRI模态为输入，预测ET、TC和全肿瘤（WT）区域，实现自动脑肿瘤分割。其中，MedNeXt作为主要分割模型，采用全局响应归一化进行特征学习；而CorrDiff则被训练为后处理残差细化方法，在最终阈值化前修正概率图中的误差。通过五折交叉验证，CoMNeT在多数肿瘤区域取得了最高的Dice分数，其中ET、TC、WT及平均Dice分数分别为0.7543±0.0261、0.6806±0.0166、0.9049±0.0128和0.7798±0.0184。CoMNeT优于两种选定的基线模型：SegResNet（平均Dice为0.7555±0.0190）和单独使用的MedNeXt（平均Dice为0.7697±0.0154）。我们的研究结果支持将修正扩散与逐折概率集成作为现有最先进3D卷积模型用于自动胶质瘤分割的实用增强手段。