Tumor Segmentation and Tumor Probability Analysis with Convolutional Neural Networks: Novel Strategies for Individualized Radiation Therapy of Head&Neck Cancer

卷积神经网络的肿瘤分割和肿瘤概率分析：头部个体化放射治疗的新策略

• 批准号: 443978314
• 负责人: Professor Dr. Michael Bock
• 金额: --
• 依托单位: Arbeitsbereich Medizinphysik
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/443978314?language=en
• 关键词: Tumor; Segmentation; Probability; Analysis; Convolutional

The manual segmentation of tumor lesions is a tedious and user-dependent task in radiation therapy planning. Automatic segmentation algorithms have been proposed as an alternative, among which convolutional neural networks (CNN) exhibit the best segmentation performance if a sufficiently large amount of independent input imagedata is available. In radiation therapy planning of head&neck tumors, multi-contrast and multimodality imaging with MRI, PET and CT is often performed with up to 9 different contrasts per exam. These images are used to delineate the tumor margins and to identify hypoxic sub-regions in the tumor which require higher radiation dosesfor an efficient treatment. In this project CNNs will be trained on existing image data of a prospective clinical trial for head&neck tumor treatment. As neither the optimal architecture nor the best setting for the internal parameters is known for CNNs, multiple CNNs will be trained and compared using existing radiation plans as ground truth.In addition, pre-processing methods will be developed to correct for systematic image heterogeneity (originating for example from MRI systems with different field strength) so that more data can be included in the study. For this, the known MRI signal equations will be used to modify the signal intensities for each tissue type individually. It is time-consuming to acquire multimodality and multicontrast images.To identify those input image data that do not improve the segmentation performance, CNNs with different input data will be trained using a leave-one-out strategy, and the CNNs will be compared with a full CNN implementation. As a result of this optimization, the imaging protocol will be optimized and a CNN with optimal performance will be realized. With the resulting CNN configuration automatic tumor and lymph node segmentation will be performed, and correlated with a panel of established biological markers from histology to assess whether CNN-derived imaging features can predict individual tumor biology. The result of this project will be an optimized CNN for automaticsegmentation of head&neck tumors which will also provide additional radiomics information about ways to shorten the diagnostic imaging protocols and about hypoxia-related tumor heterogeneity, which might have direct consequences for new strategies to minimize tumor recurrence.

在放射治疗计划中，肿瘤病灶的手动分割是一项繁琐且依赖于用户的任务。自动分割算法已被提出作为替代方案，其中如果有足够大量的独立输入图像数据可用，卷积神经网络（CNN）将表现出最佳的分割性能。在头颈肿瘤的放射治疗计划中，通常使用 MRI、PET 和 CT 进行多对比和多模态成像，每次检查最多使用 9 个不同的对比。这些图像用于描绘肿瘤边缘并识别肿瘤中需要更高辐射剂量才能进行有效治疗的缺氧子区域。在该项目中，CNN 将接受头颈肿瘤治疗前瞻性临床试验的现有图像数据的训练。由于 CNN 的最佳架构和内部参数的最佳设置均未知，因此将使用现有辐射计划作为基本事实来训练和比较多个 CNN。此外，将开发预处理方法来校正系统图像异质性（例如源自具有不同场强的 MRI 系统），以便在研究中包含更多数据。为此，已知的 MRI 信号方程将用于单独修改每种组织类型的信号强度。获取多模态和多对比度图像非常耗时。为了识别那些不能提高分割性能的输入图像数据，将使用留一策略来训练具有不同输入数据的CNN，并将CNN与完整的CNN实现进行比较。通过这种优化，成像协议将得到优化，并且将实现具有最佳性能的 CNN。利用由此产生的 CNN 配置，将执行自动肿瘤和淋巴结分割，并与一组已建立的组织学生物标记相关联，以评估 CNN 衍生的成像特征是否可以预测个体肿瘤生物学。该项目的结果将是一个用于头颈肿瘤自动分割的优化 CNN，它还将提供有关缩短诊断成像方案的方法和缺氧相关肿瘤异质性的额外放射组学信息，这可能对减少肿瘤复发的新策略产生直接影响。