Glioblastoma (GBM) is the commonest primary malignant brain tumor in adults, and despite advances in multi-modality therapy, the outlook for patients has changed little in the last 10 years. Local recurrence is the predominant pattern of treatment failure, hence improved local therapies (surgery and radiotherapy) are needed to improve patient outcomes. Currently segmentation of GBM for surgery or radiotherapy (RT) planning is labor intensive, especially for high-dimensional MR imaging methods that may provide more sensitive indicators of tumor phenotype. Automating processing and segmentation of these images will aid treatment planning. Diffusion tensor magnetic resonance imaging is a recently developed technique (DTI) that is exquisitely sensitive to the ordered diffusion of water in white matter tracts. Our group has shown that decomposition of the tensor information into the isotropic component (p – shown to represent tumor invasion) and the anisotropic component (q – shown to represent the tumor bulk) can provide valuable prognostic information regarding tumor infiltration and patient survival. However, tensor decomposition of DTI data is not commonly used for neurosurgery or radiotherapy treatment planning due to difficulties in segmenting the resultant image maps. For this reason, automated techniques for segmentation of tensor decomposition maps would have significant clinical utility. In this paper, we modified a well-established convolutional neural network architecture (CNN) for medical image segmentation and used it as an automatic multi-sequence GBM segmentation based on both DTI image maps (p and q maps) and conventional MRI sequences (T2-FLAIR and T1 weighted post contrast (T1c)). In this proof-of-concept work, we have used multiple MRI sequences, each with individually defined ground truths for better understanding of the contribution of each image sequence to the segmentation performance. The high accuracy and efficiency of our proposed model demonstrates the potential of utilizing diffusion MR images for target definition in precision radiation treatment planning and surgery in routine clinical practice.

胶质母细胞瘤 (GBM) 是成人中最常见的原发性恶性脑肿瘤，尽管多模式治疗取得了进展，但患者的前景在过去 10 年中几乎没有变化。局部复发是治疗失败的主要模式，因此需要改进局部治疗（手术和放疗）来改善患者的治疗效果。目前，用于手术或放疗 (RT) 计划的 GBM 分割是劳动密集型的，特别是对于可以提供更敏感的肿瘤表型指标的高维 MR 成像方法。这些图像的自动处理和分割将有助于治疗计划。扩散张量磁共振成像是一项最近开发的技术（DTI），它对白质束中水的有序扩散非常敏感。我们的小组已经证明，将张量信息分解为各向同性分量（p - 显示代表肿瘤侵袭）和各向异性分量（q - 显示代表肿瘤体积）可以提供有关肿瘤浸润和患者生存的有价值的预后信息。然而，由于难以分割结果图像图，DTI 数据的张量分解并不常用于神经外科或放射治疗治疗计划。因此，用于张量分解图分割的自动化技术将具有重要的临床实用性。在本文中，我们修改了用于医学图像分割的成熟卷积神经网络架构（CNN），并将其用作基于 DTI 图像图（p和q图）和传统 MRI 序列（T2）的自动多序列 GBM 分割。 -FLAIR 和 T1 加权后对比 (T1c))。在这项概念验证工作中，我们使用了多个 MRI 序列，每个序列都有单独定义的基本事实，以便更好地理解每个图像序列对分割性能的贡献。我们提出的模型的高精度和高效率证明了利用扩散 MR 图像在常规临床实践中精确放射治疗计划和手术中进行目标定义的潜力。