T2-weighted fluid-attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) scans are important for diagnosis, treatment planning and monitoring of brain tumors. Depending on the brain tumor type, the FLAIR hyperintensity volume is an important measure to asses the tumor volume or surrounding edema, and an automatic segmentation of this would be useful in the clinic. In this study, around 5000 FLAIR images of various tumors types and acquisition time points from different centers were used to train a unified FLAIR hyperintensity segmentation model using an Attention U-Net architecture. The performance was compared against dataset specific models, and was validated on different tumor types, acquisition time points and against BraTS. The unified model achieved an average Dice score of 88.65\% for pre-operative meningiomas, 80.08% for pre-operative metastasis, 90.92% for pre-operative and 84.60% for post-operative gliomas from BraTS, and 84.47% for pre-operative and 61.27\% for post-operative lower grade gliomas. In addition, the results showed that the unified model achieved comparable segmentation performance to the dataset specific models on their respective datasets, and enables generalization across tumor types and acquisition time points, which facilitates the deployment in a clinical setting. The model is integrated into Raidionics, an open-source software for CNS tumor analysis.

翻译：T2加权液体衰减反转恢复（FLAIR）磁共振成像（MRI）扫描对于脑肿瘤的诊断、治疗规划和监测具有重要意义。根据脑肿瘤类型的不同，FLAIR高信号体积是评估肿瘤体积或周围水肿的重要指标，其自动分割在临床中具有实用价值。本研究利用来自不同中心的约5000幅涵盖多种肿瘤类型及采集时间点的FLAIR图像，采用Attention U-Net架构训练了一个统一的FLAIR高信号分割模型。该模型的性能与针对特定数据集训练的模型进行了比较，并在不同肿瘤类型、采集时间点以及BraTS数据集上进行了验证。统一模型在BraTS数据集中取得了以下平均Dice分数：术前脑膜瘤88.65%、术前转移瘤80.08%、术前胶质瘤90.92%与术后胶质瘤84.60%，以及术前低级别胶质瘤84.47%与术后低级别胶质瘤61.27%。此外，结果表明统一模型在各自数据集上达到了与特定数据集模型相当的分割性能，并能够实现跨肿瘤类型和采集时间点的泛化，这有助于其在临床环境中的部署。该模型已集成至中枢神经系统肿瘤分析开源软件Raidionics中。