GLISTRboost: Combining Multimodal MRI Segmentation, Registration, and Biophysical Tumor Growth Modeling with Gradient Boosting Machines for Glioma Segmentation.
GLISTRboost: Combining Multimodal MRI Segmentation, Registration, and Biophysical Tumor Growth Modeling with Gradient Boosting Machines for Glioma Segmentation.
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DOI：
10.1007/978-3-319-30858-6_13
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发表时间：
2016-01-01
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影响因子：
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通讯作者：
Davatzikos, Christos
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作者：
Bakas, Spyridon;Zeng, Ke;Davatzikos, Christos
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神经胶质瘤的方法。所提出的方法基于混合生成判别模型。首先，基于期望最大化框架的生成方法结合了神经胶质瘤生长模型，用于将脑部扫描分割为肿瘤以及健康组织标签。其次，使用梯度增强多类分类方案根据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根据来自多种模式的患者特定强度统计数据进一步细化和最终确定肿瘤分割。我们在 2015 年 BRAin 肿瘤分割 (BRATS) 挑战赛的训练阶段对 186 个病例的方法进行了评估，并报告了有希望的结果。在测试阶段，该算法在 53 个未见过的情况下进行了额外评估，在竞争方法中取得了最佳性能。
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.