Accurate segmentation of different sub-regions of gliomas such as peritumoral edema, necrotic core, enhancing, and non-enhancing tumor core from multimodal MRI scans has important clinical relevance in diagnosis, prognosis and treatment of brain tumors. However, due to the highly heterogeneous appearance and shape of these tumors, segmentation of the sub-regions is challenging. Recent developments using deep learning models has proved its effectiveness in various semantic and medical image segmentation tasks, many of which are based on the U-Net network structure with symmetric encoding and decoding paths for end-to-end segmentation due to its high efficiency and good performance. In brain tumor segmentation, the 3D nature of multimodal MRI poses challenges such as memory and computation limitations and class imbalance when directly adopting the U-Net structure. In this study we aim to develop a deep learning model using a 3D U-Net with adaptations in the training and testing strategies, network structures, and model parameters for brain tumor segmentation. Furthermore, instead of picking one best model, an ensemble of multiple models trained with different hyper-parameters are used to reduce random errors from each model and yield improved performance. Preliminary results demonstrate the effectiveness of this method and achieved the 9th place in the very competitive 2018 Multimodal Brain Tumor Segmentation (BraTS) challenge. In addition, to emphasize the clinical value of the developed segmentation method, a linear model based on the radiomics features extracted from segmentation and other clinical features are developed to predict patient overall survival. Evaluation of these innovations shows high prediction accuracy in both low-grade glioma and glioblastoma patients, which achieved the 1st place in the 2018 BraTS challenge.

中文翻译：

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使用 3D U-Nets 集成的脑肿瘤分割和使用放射组学特征的整体生存预测

从多模态 MRI 扫描中准确分割胶质瘤的不同子区域，例如肿瘤周围水肿、坏死核心、增强和非增强肿瘤核心，在脑肿瘤的诊断、预后和治疗中具有重要的临床意义。然而，由于这些肿瘤的外观和形状高度异质，子区域的分割具有挑战性。使用深度学习模型的最新发展已经证明了其在各种语义和医学图像分割任务中的有效性，其中许多基于 U-Net 网络结构，具有对称的编码和解码路径进行端到端分割，因为它具有高效率和高效率。很好的表现。在脑肿瘤分割中，当直接采用 U-Net 结构时，多模态 MRI 的 3D 特性带来了诸如内存和计算限制以及类别不平衡等挑战。在这项研究中，我们的目标是使用 3D U-Net 开发深度学习模型，并在训练和测试策略、网络结构和脑肿瘤分割模型参数方面进行调整。此外，不是选择一个最佳模型，而是使用使用不同超参数训练的多个模型的集合来减少每个模型的随机错误并提高性能。初步结果证明了该方法的有效性，并在竞争非常激烈的 2018 年多模式脑肿瘤分割 (BraTS) 挑战中获得第 9 名。此外，为了强调所开发的分割方法的临床价值，开发基于从分割和其他临床特征中提取的放射组学特征的线性模型来预测患者的总体存活率。对这些创新的评估表明，在低级别胶质瘤和胶质母细胞瘤患者中均具有较高的预测准确率，在 2018 年 BraTS 挑战中获得第一名。