Bakas Spyridon, Zeng Ke, Sotiras Aristeidis, Rathore Saima, Akbari Hamed, Gaonkar Bilwaj, Rozycki Martin, Pati Sarthak, Davatzikos Christos
Section of Biomedical Image Analysis, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA.
Brainlesion. 2016;9556:144-155. doi: 10.1007/978-3-319-30858-6_1.
We present an approach for segmenting low- and high-grade gliomas in multimodal magnetic resonance imaging volumes. The proposed approach is based on a hybrid generative-discriminative model. Firstly, a generative approach based on an Expectation-Maximization framework that incorporates a glioma growth model is used to segment the brain scans into tumor, as well as healthy tissue labels. Secondly, a gradient boosting multi-class classification scheme is used to refine tumor labels based on information from multiple patients. Lastly, a probabilistic Bayesian strategy is employed to further refine and finalize the tumor segmentation based on patient-specific intensity statistics from the multiple modalities. We evaluated our approach in 186 cases during the training phase of the BRAin Tumor Segmentation (BRATS) 2015 challenge and report promising results. During the testing phase, the algorithm was additionally evaluated in 53 unseen cases, achieving the best performance among the competing methods.
我们提出了一种在多模态磁共振成像体积中分割低级别和高级别胶质瘤的方法。所提出的方法基于一种混合生成-判别模型。首先，基于期望最大化框架并结合胶质瘤生长模型的生成方法用于将脑部扫描分割为肿瘤以及健康组织标签。其次，使用梯度提升多类分类方案，基于来自多个患者的信息来细化肿瘤标签。最后，采用概率贝叶斯策略，基于来自多种模态的患者特定强度统计信息进一步细化并最终确定肿瘤分割。在脑肿瘤分割（BRATS）2015挑战赛的训练阶段，我们在186例病例中评估了我们的方法，并报告了有前景的结果。在测试阶段，该算法在53例未见病例中进行了额外评估，在竞争方法中取得了最佳性能。
