Deep LOGISMOS

摘要： 这是一个项目的竞争性延续，该项目已经产生了高度灵活、准确和 广泛适用的上下文感知n维图像分割的LOGISMOS框架。至 实质性地改进和扩展其能力，我们将开发结合了 强化了LOGISMOS和深度学习(DL)的互补优势。 越来越需要无故障的定量3D和高维图像分析来进行诊断和/或 规划目的。目前使用的例子有放射肿瘤学、心脏病学、眼科和 常规临床医学的其他领域，然而，其中许多领域仍然依赖于手动逐层追踪。 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 将解决这一问题，并将提供常规有效的临床图像分析。 可分析的品质。 为了刺激这个研究项目的新阶段，我们假设：先进的基于图形的图像 分段算法，当与深度学习派生的应用/通道特定时 参数，并允许高效的专家-分析师指导与 分割算法，将在常规情况下显著提高定量分析性能 跨不同应用领域获取的、复杂的、诊断性质量的医学图像。 提出的研究重点是建立一个图像分割和分析框架 结合LOGISMOS和DL的优点，开发一种高效生成训练的新方法 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。 我们将实现以下具体目标： 1.提出了一种构建3D、4D、n-D大型分割训练数据集的有效方法 使用辅助性和提示性注释。 2.开发了深度LOGISMOS，结合了两种成熟的算法策略-深度学习 和LOGISMOS图搜索。 3.开发和验证深度学习的质量控制方法。 4.在与医疗保健相关的应用中，演示Deep LOGISMOS改进了细分 与最先进的分割技术相比较的性能。 Deep LOGISMOS将带来广泛可用的临床图像常规量化，这是积极的 影响可靠的基于图像的信息在未来精确医学中的作用。

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.