Adaptive Large-Scale Framework for Automatic Biomedical Image Segmentation

自动生物医学图像分割的自适应大规模框架

• 批准号: 9119513
• 负责人: Paul A. Yushkevich
• 金额: $ 59.77万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-01 至 2019-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9119513
• 关键词: Address; Adopted; Affect; Algorithms; Atlases; Biomedical Research; Brain; Brain imaging; Cardiac; Clinical; Clinical Data; Clinical Research; Cloud Computing; Communities; Complex; Computational algorithm; Computer Vision Systems; Computer software; Consensus; Custom; Data; Data Set; Dementia; Diagnostic; Evaluation; Gold; Health; Heterogeneity; High Performance Computing; Hippocampus (Brain); Image; Image Analysis; International; Intervention Studies; Joints; Label; Lead; Learning; Lesion; Literature; Magnetic Resonance Imaging; Manuals; Measurement; Measures; Medial; Medical Imaging; Medical Research; Methodology; Methods; Modality; Modeling; Multiple Sclerosis Lesions; Myocardium; Paper; Pathology; Patient Care; Performance; Pharmacologic Substance; Public Domains; Research; Research Infrastructure; Research Personnel; S-nitro-N-acetylpenicillamine; Scheme; Services; Structure; Techniques; Technology; Temporal Lobe; Temporal Lobe Epilepsy; Time; Training; Ultrasonography; Uncertainty; Validation; Work; aortic valve; base; bioimaging; cardiovascular visualization; clinical application; clinical phenotype; clinical practice; cloud based; cohort; cost; diagnostic accuracy; experience; image processing; image registration; imaging Segmentation; imaging modality; improved; innovation; interest; multi-atlas segmentation; new technology; novel; open source; outreach; research study; success; targeted imaging; tool; tumor

DESCRIPTION (provided by applicant): Multi-atlas label fusion (MALF) is a powerful new technology that can automatically detect and label anatomical structures in biomedical images. It is arguably the most successful general-purpose automatic image segmentation technique ever developed. Automatic segmentation is in high demand in clinical and research applications of medical imaging, since segmentation forms a crucial step towards extracting quantitative information from imaging data, and since manual and semi-automatic approaches are ill suited for today's increasingly large and complex imaging datasets. Despite a number of papers that demonstrated outstanding performance of MALF methods across a range of biomedical imaging applications, the broader biomedical imaging research community has been slow to adopt this technique. This can be explained by multiple factors, including the technique's high computational demands, lack of a turnkey software implementation, as well as scarcity of validation in clinical imaging datasets and in the presence of extensive pathology. The present application seeks to remove these barriers and to enable a broad range of clinicians and biomedical researchers to take advantage of MALF technology. It builds on our strong track record of innovation in the MALF field, including a novel redundancy-correcting MALF technique that led in segmentation grand challenges in the past two years. Aim 1 seeks to improve the computational performance of MALF by replacing dense deformable image registration, by far the most time consuming component of MALF, with faster and less constrained sparse registration strategies. We hypothesize that this will not only reduce the computational cost of MALF, but will also make it more robust to anatomical variability, in particular enabling its use for tumor and lesion segmentation. Aim 2 proposes algorithmic extensions to MALF that support automatic segmentation of dynamic and multi-modality imaging datasets, which have been largely overlooked in the MALF literature. Aim 3 will develop a turnkey open-source implementation of MALF methodology. Taking advantage of cloud computing technology, this software will allow users with minimal image processing expertise to take full advantage of MALF segmentation on their desktop. Aim 3 will also provide a set of publicly available atlases and the means for users to build new custom atlas sets from their own data. Aim 4 will perform extensive evaluation of the new methods and software in challenging real-world clinical imaging data, including brain and cardiac imaging. As part of this evaluation, we will quantify how well our MALF approach and competing techniques generalize to novel imaging datasets with heterogeneity in acquisition parameters and clinical phenotypes.

描述（由申请人提供）：多图谱标记融合（MALF）是一种功能强大的新技术，可以自动检测和标记生物医学图像中的解剖结构。它可以说是有史以来最成功的通用自动图像分割技术。自动分割在医学成像的临床和研究应用中具有很高的需求，因为分割形成了从成像数据中提取定量信息的关键步骤，并且因为手动和半自动方法不适合当今日益庞大和复杂的成像数据集。尽管许多论文证明了MALF方法在一系列生物医学成像应用中的出色性能，但更广泛的生物医学成像研究界采用这种技术的速度很慢。这可以通过多种因素来解释，包括该技术的高计算需求，缺乏交钥匙软件实现，以及临床成像数据集和存在广泛病理学的情况下缺乏验证。本申请试图消除这些障碍，并使广泛的临床医生和生物医学研究人员能够利用MALF技术。它建立在我们在MALF领域创新的良好记录之上，包括一种新颖的冗余校正MALF技术，该技术在过去两年中带来了分割的巨大挑战。目标1旨在通过用更快、约束更少的稀疏配准策略取代密集可变形图像配准（迄今为止MALF中最耗时的部分）来提高MALF的计算性能。我们假设，这不仅会降低MALF的计算成本，而且还将使其对解剖变异性更鲁棒，特别是使其能够用于肿瘤和病变分割。目标2提出了MALF的算法扩展，支持动态和多模态成像数据集的自动分割，这在MALF文献中很大程度上被忽视了。目标3将开发MALF方法的交钥匙开源实施。利用云计算技术，该软件将允许具有最低图像处理专业知识的用户在其桌面上充分利用MALF分割。Aim 3还将提供一套公开的地图集，并为用户提供根据自己的数据建立新的自定义地图集的方法。Aim 4将对新方法和软件进行广泛的评估，以挑战真实世界的临床成像数据，包括大脑和心脏成像。作为该评价的一部分，我们将量化我们的MALF方法和竞争技术推广到具有采集参数和临床表型异质性的新型成像数据集的效果。