Deep LOGISMOS

深度逻辑

• 批准号: 10016301
• 负责人: JOHN M. BUATTI
• 金额: $ 39.63万
• 依托单位: UNIVERSITY OF IOWA
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-04-01 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10016301
• 关键词: 3-Dimensional; Address; Adoption; Age related macular degeneration; Algorithms; Angiography; Area; Automation; Awareness; Biomedical Computing; Cardiac; Cardiology; Cardiovascular system; Caring; Clinical; Clinical Medicine; Clinical Research; Complex; Computational Science; Consumption; Data; Data Set; Development; Diagnostic; Diagnostic Neoplasm Staging; FDA approved; Failure; Fundus photography; Generations; Glaucoma; Goals; Graph; Healthcare; Human; Image; Image Analysis; Individual; Learning; Location; Malignant Neoplasms; Manuals; Medical; Medical Imaging; Medicine; Methods; Modality; Morphology; Myocardial Infarction; Nature; Ophthalmology; Organ; PET/CT scan; Patient Care; Patients; Performance; Phase; Problem Solving; Publications; Quality Control; Radiation Oncology; Research; Research Project Grants; Retina; Role; Slice; Stroke; Suggestion; Surface; Techniques; Technology; Three-dimensional analysis; Time; Tissues; Training; Tumor Tissue; adjudication; automated analysis; automated segmentation; base; bioimaging; clinical care; clinical imaging; clinical practice; deep learning; diabetic; experience; flexibility; imaging Segmentation; imaging modality; improved; innovation; insight; learning strategy; macular edema; n-dimensional; precision medicine; response; segmentation algorithm; success; task analysis; treatment planning

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.

抽象的： 这是一个竞争性的项目，该项目使一年高度灵活，准确，并且 广泛适用的logismos框架，用于上下文感知的n性图像分段 我们将大大提高和扩展其功能，我们将开发结合和 加强了Logismos和深度学习（DL）的编译优势。 对诊断和或或或或或或或或或或或ob的无定量故障3D和更高D图像分析的需求越来越大 计划目的。 但是，其他常规临床医学领域，许多仍依靠手动切片逐片跟踪。 这种体系的这种手动性质阻碍了它们在精确医学中的使用 在这里提供的支架解决了问题，并将提供常规的临床图像功效 可分析的质量。 为了刺激该研究项目的新阶段，我们假设：高级基于图的图像 分割算法，同时与特定于深度学习的应用程序/模式相结合 参数并允许高效的专家分析师指导与 分割算法将显着提高常规的定量分析性能 在各种应用区域中获得的，复杂的，复杂的诊断质量医学图像。 拟议的研究重点是建立图像细分和分析框架 结合Logismos和DL的优势，开发一种有效产生培训的新方法 从示例中学习所需的数据，形成3D，4D和通常N-D的无故障策略 定量医学图像分析，并发现自动段细分市场质量控制的方法。 我们将实现这些具体目标： 1。开发一种在3D，4D，N-D中构建大型培训数据集的有效方法 使用辅助和暗示性注释。 2。发​​展深厚的logismos，结合了两个完善的算法策略深度学习 和Logismos图搜索。 3。开发和验证方法采用深度学习来控制深层logismos的方法。 4。在与医疗保健相关的应用中，证明了深层logismos改善细分 与最先进的细分技术相比，性能。 深层logismos将对临床图像的常规量化，积极地量化 影响可靠的基于图像的信息在明天的精密医学中的作用。