Brain Tumor Segmentation and Survival Prediction Using Multimodal MRI Scans With Deep Learning
Brain Tumor Segmentation and Survival Prediction Using Multimodal MRI Scans With Deep Learning
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DOI：
10.3389/fnins.2019.00810
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发表时间：
2019-08-16
影响因子：
4.3
通讯作者：
Luo, Lin
中科院分区：
文献类型：
--
作者：
Sun, Li;Zhang, Songtao;Luo, Lin
胶质瘤是最常见的脑部原发恶性肿瘤。准确和稳健的肿瘤分割和对患者总体生存的预测对于诊断、治疗计划和危险因素识别非常重要。在这里，我们提出了一个基于深度学习的框架，用于脑胶质瘤的脑肿瘤分割和生存预测，使用多模式MRI扫描。对于肿瘤分割，我们使用三种不同3D CNN架构的集成，通过多数规则获得稳健的性能。这种方法可以有效地减少模型偏差，提高性能。对于生存预测，我们从分割的肿瘤区域中提取4524个放射学特征，然后使用决策树和交叉验证来选择有效的特征。最后，训练一个随机森林模型来预测患者的总存活率。在2018年MICCAI多模式脑瘤分割挑战赛(BRATS)中，我们的方法在生存预测任务和分割任务的60+参赛团队中分别排名第二和第五，在短期幸存者、中期幸存者和长期幸存者的分类上取得了有希望的61.0%的准确率。
Gliomas are the most common primary brain malignancies. Accurate and robust tumor segmentation and prediction of patients' overall survival are important for diagnosis, treatment planning and risk factor identification. Here we present a deep learning-based framework for brain tumor segmentation and survival prediction in glioma, using multimodal MRI scans. For tumor segmentation, we use ensembles of three different 3D CNN architectures for robust performance through a majority rule. This approach can effectively reduce model bias and boost performance. For survival prediction, we extract 4,524 radiomic features from segmented tumor regions, then, a decision tree and cross validation are used to select potent features. Finally, a random forest model is trained to predict the overall survival of patients. The 2018 MICCAI Multimodal Brain Tumor Segmentation Challenge (BraTS), ranks our method at 2nd and 5th place out of 60+ participating teams for survival prediction tasks and segmentation tasks respectively, achieving a promising 61.0% accuracy on the classification of short-survivors, mid-survivors and long-survivors.