提出可同时分割肿瘤与健康脑组织并输出置信度的统一模型
Enhancing Neuro-Oncology Through Self-Assessing Deep Learning Models for Brain Tumor Unified Model for MRI Segmentation
- 在nnUNet基础上增加体素级不确定性通道，单次推理完成
- 肿瘤DSC达0.86，全脑结构DSC达0.81，不确定性相关性0.750
- 首次实现肿瘤+周围正常结构+不确定性图一体化输出，适合临床决策
准确分割脑肿瘤对诊断、手术规划和治疗监测至关重要。深度学习虽在基准上取得进展，但两大问题限制其临床应用：缺乏错误的不确定性估计，且未对肿瘤周围的健康脑结构进行分割以支持手术。现有方法无法统一肿瘤定位与解剖上下文，也缺少置信度评分。本研究提出一种不确定性感知框架，在nnUNet中引入体素级不确定性通道。在BraTS2023数据集上训练，不确定性预测的相关系数为0.750，均方根偏差为0.047，且不损害肿瘤分割精度。该方法可在单次前向传播中完成不确定性预测，无需额外网络或推理。针对全脑解剖上下文，构建统一模型融合正常与癌症数据集，实现脑结构DSC 0.81、肿瘤DSC 0.86，关键区域表现稳健。两项创新结合，首次实现肿瘤在自然解剖环境中的分割，并叠加不确定性热力图。可视化分析显示，不确定性信息能有效揭示预测缺陷，辅助医生做出更明智的手术决策。
原文摘要 · Abstract (English)
Accurate segmentation of brain tumors is vital for diagnosis, surgical planning, and treatment monitoring. Deep learning has advanced on benchmarks, but two issues limit clinical use: no uncertainty estimates for errors and no segmentation of healthy brain structures around tumors for surgery. Current methods fail to unify tumor localization with anatomical context and lack confidence scores. This study presents an uncertainty-aware framework augmenting nnUNet with a channel for voxel-wise uncertainty. Trained on BraTS2023, it yields a correlation of 0.750 and RMSD of 0.047 for uncertainty without hurting tumor accuracy. It predicts uncertainty in one pass, with no extra networks or inferences, aiding clinical decisions. For whole-brain context, a unified model combines normal and cancer datasets, achieving a DSC of 0.81 for brain structures and 0.86 for tumor, with robust key-region performance. Combining both innovations gives the first model outputting tumor in natural surroundings plus an overlaid uncertainty map. Visual checks of outputs show uncertainty offers key insights to evaluate predictions and fix errors, helping informed surgical decisions from AI.
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