Deep LOGISMOS

摘要： 这是一个有竞争力的项目，已经产生了高度灵活，准确， 广泛适用的LOGISMOS框架，用于上下文感知的n维图像分割。到 我们将开发深度LOGISMOS， 强化了LOGISMOS和深度学习（DL）的互补优势。 对于用于诊断和/或诊断的定量无故障3D和更高D图像分析的需求日益增长。 规划目的。目前使用的实例存在于放射肿瘤学、心脏病学、眼科学和放射治疗学中。 常规临床医学的其他领域，然而，其中许多仍然依赖于手动逐片追踪。 这种分析的手动性质阻碍了它们在精确医学中的使用。深度LOGISMOS研究 这里提出的将解决这个问题，并将提供常规的有效分析的临床图像， 可分析的质量。 为了刺激这个研究项目的新阶段，我们假设：高级基于图形的图像 分割算法，当与深度学习衍生的应用程序/特定模态相结合时 参数，并允许高效的专家分析指导与 分割算法，将显着提高定量分析性能，在常规 在不同的应用领域中，采集复杂的诊断质量的医学图像。 本文的研究重点是建立一个图像分割和分析框架 结合LOGISMOS和DL的优势，开发一种有效生成培训的新方法 从示例中学习所需的数据，形成3D，4D和一般n-D的无故障策略 定量医学图像分析，并发现自动分割质量控制的方法。 我们将实现这些具体目标： 1.开发一种高效的方法，用于构建3D、4D、n-D的大型分割训练数据集 使用辅助和暗示性注释。 2.开发深度LOGISMOS，结合两种成熟的算法策略-深度学习 logismos graph search。 3.开发和验证采用深度学习进行深度LOGISMOS质量控制的方法。 4.在医疗保健相关应用中，证明Deep LOGISMOS可改善分割 与最先进的分割技术相比， 深度LOGISMOS将带来广泛可用的临床图像的常规量化， 影响可靠的基于图像的信息在未来精准医疗中的作用。

Abstract: This is a competitive continuation of a project that already yielded the highly flexible, accurate, and broadly applicable LOGISMOS framework for context-aware n-dimensional image segmentation. To substantially improve and extend its capability, we will develop Deep LOGISMOS that combines and reinforces the complementary advantages of LOGISMOS and deep learning (DL). There is growing need for quantitative failure-free 3D and higher-D image analysis for diagnostic and/or planning purposes. Examples of current use exist in radiation oncology, cardiology, ophthalmology and other areas of routine clinical medicine, many of which however still rely on manual slice-by-slice tracing. This manual nature of such analyses hinders their use in precision medicine. Deep LOGISMOS research proposed here will solve this problem and will offer routine efficient analysis of clinical images of analyzable quality. To stimulate a new phase of this research project, we hypothesize that: Advanced graph-based image segmentation algorithms, when combined with deep-learning-derived application/modality specific parameters and allowing highly efficient expert-analyst guidance working in concert with the segmentation algorithms, will significantly increase quantitative analysis performance in routinely acquired, complex, diagnostic-quality medical images across diverse application areas. The proposed research focuses on establishing an image segmentation and analysis framework combining the strengths of LOGISMOS and DL, developing a new way to efficiently generate training data necessary for learning from examples, forming a failure-free strategy for 3D, 4D, and generally n-D quantitative medical image analysis, and discovering ways for automated segmentation quality control. We will fulfill these specific aims: 1. Develop an efficient approach for building large segmentation training datasets in 3D, 4D, n-D using assisted and suggestive annotations. 2. Develop Deep LOGISMOS, combining two well-established algorithmic strategies – deep learning and LOGISMOS graph search. 3. Develop and validate methods employing deep learning for quality control of Deep LOGISMOS. 4. In healthcare-relevant applications, demonstrate that Deep LOGISMOS improves segmentation performance in comparison with state-of-the-art segmentation techniques. Deep LOGISMOS will bring broadly available routine quantification of clinical images, positively impacting the role of reliable image-based information in tomorrow’s precision medicine.