Fast and Robust Deep Learning for Medical imaging: Segmentation and Registration methods invariant to contrast and resolution

项目摘要 标题 快速稳健的医学成像深度学习：不变的分割和配准方法 预测和解决。 摘要 分割和配准是广泛的科学研究中的关键任务，并且已经被广泛地 在成像分析框架中实施。不幸的是，大多数现有的工具都有两个重要的缺点-- Back：它们对计算要求很高，而且通常会对图像类型施加限制 可以准确分析的数据。虽然前面的缺点最近已经通过使用 一旦被训练，这些系统就会放大后者，后者仍然是一种 重大限制。这通常意味着工具仅在非常有限的扫描类型范围上产生准确的结果， 最常见的是那些他们接受过培训的人，他们容易受到这些数据中存在的重复偏见的影响。为 分割这是一项特别繁重的工作，因为训练经常需要手动标注以下内容的表示 不同类型的输入数据。 图像类型的约束在很大程度上制约了图像分析及其下游影响 域名。例如，在研究成像方面，它限制了必须保持采集的多点和纵向研究 协议保持不变或试图协调不同采集平台之间的协议，甚至此过程 当差异太大时(例如，在场强上)，成功有限。调查人员经常需要 调整、重新设计或重新培训工具以完成其预期任务以及可用的图像和手册标签，主要 更多的分析障碍。从分析临床来源中也可以获得丰富的知识。 磁共振成像，这可能有助于更好地了解许多疾病过程的生物学基础 以及更准确地量化治疗干预的效果。但是，作为一部分获取的扫描 常规的临床护理通常具有不同的对比度、明显较低的分辨率以及由于噪声而导致的质量较低 或主题动议。几乎没有公开可用的工具可以处理广泛的收购 典型临床影像的变异性。 我们建议设计和分发基于机器学习的工具来完全消除这些障碍。我们会 开发图像分割和配准深度学习方法，在不正确的情况下保持其准确性 无需训练数据或对每个对比度或分辨率进行网络微调，即可实现大多数对比度或分辨率的集中扫描 数据差异。我们将在基于学习的分割、配准、同步方法的最新工作基础上再接再厉。 论文和增强以利用神经网络的速度、MR物理模型的丰富性和 概率贝叶斯模型的泛化能力。我们将在一个大型综合性多站点上验证这些工具 这项研究纳入了跨越不同年龄、性别和种族的扫描的新手动标签。最后，我们将部署 他们在一项回溯性卒中队列中分析解剖和白质损害。新的技术将是 既作为独立的开源软件实现，也作为自由冲浪分析包的一部分实现， 数以千计的方法开发人员以及科学和临床研究人员都可以免费使用它们。

Project Summary Title Fast and Robust Deep Learning for Medical imaging: Segmentation and Registration methods invariant to con- trast and resolution. Summary Segmentation and registration are critical tasks in a broad range of scientific studies, and have been widely implemented in imaging analysis frameworks. Unfortunately, most existing tools suffer from two important draw- backs: they are computationally demanding, and most often impose limiting restrictions on the type of image data that can be accurately analyzed. While the former drawback has been recently addressed through the use of deep neural networks that execute rapidly once trained, these systems amplify the latter, which remains a major restriction. This typically means that tools only yield accurate results on a very limited range of scan types, most commonly those that they were trained on and are susceptible to repeating bias present in those data. For segmentation this is particularly burdensome as training frequently requires manually labeled representations for different types of input data. The constraint of image type greatly restricts image analysis and its downstream impact in an array of important domains. For example, in research imaging, it limits multi-site and longitudinal studies that must hold acquisition protocols constant or attempt to harmonize protocols across different acquisition platforms, and even this process has limited success when the differences are too extreme (e.g. across field strength). Investigators often need to adjust, redesign, or retrain the tools for their intended tasks and available images and manual labels, leading to more barriers to analysis. There is also a wealth of knowledge to be gained from analyzing clinically-sourced MR images, which could lead to better understanding of the biological underpinnings of many disease processes and a more precise quantification of the efficacy of therapeutic interventions. However, scans acquired as part of routine clinical care are often of diverse contrast, significantly lower resolution, and lower quality due to noise or subject motion. There are few if any publicly available tools that can handle the wide range of acquisition variability in typical clinical imaging. We propose to design and distribute machine learning based tools to completely remove these barriers. We will develop imaging segmentation and registration deep learning methods that retain their accuracy given unpro- cessed scans of most contrasts or resolution without the need for training data or network fine-tuning to each data variation. We will build on our recent work in learning-based methods for segmentation, registration, syn- thesis, and augmentation to leverage the speed of neural networks, the richness of MR physics models, and the generalizability of probabilistic Bayesian models. We will validate these tools on a large comprehensive multi-site study incorporating new manual labeling of scans spanning different age, sex, and race . Finally, we will deploy them to analyze anatomy and white matter lesions in a retrospective stroke cohort. The novel techniques will be implemented both as standalone open source software as well as part of FreeSurfer analysis package, making them freely available to thousands of method developers as well as science and clinical researchers.