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

基本信息

批准号：
9350173
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
项目类别：
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/9350173
关键词：
Address Adopted Affect Algorithms Anatomy Atlases Biomedical Research Brain Brain imaging Cardiac Clinical Data Clinical Research Cloud Computing Communities Complex Computational algorithm Computer Vision Systems Computer software Consensus Custom Data Data Set Dementia Diagnostic Evaluation Gold Heterogeneity High Performance Computing Hippocampus (Brain)Image Image Analysis International Intervention Joints Label Lead Learning Lesion Literature Magnetic Resonance Imaging Manuals Measurement Measures Medial Medical Imaging Medical Research Methodology Methods Modality Modeling Multimodal Imaging Multiple Sclerosis Lesions Myocardium Paper Pathologic 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 imaging clinical phenotype clinical practice cloud based cluster computing cohort cost diagnostic accuracy experience image processing image registration imaging Segmentation imaging modality improved innovation interest multi-atlas segmentation multidisciplinary new technology novel open source outreach public health relevance research study success targeted imaging tool tumor

项目摘要

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）：多图谱标签融合（MALF）是一种强大的新技术，可以自动检测和标记生物医学图像中的解剖结构。它可以说是迄今为止开发的最成功的通用自动图像分割技术。医学成像的临床和研究应用对自动分割的需求很高，因为分割是从成像数据中提取定量信息的关键一步，而且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。尽管有许多论文证明了 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在采用这项技术方面进展缓慢。这可以用多种因素来解释，包括该技术的高计算要求、缺乏交钥匙软件实施、以及临床成像数据集和广泛病理学中缺乏验证。本申请旨在消除这些障碍，并使广大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 领域的强大创新记录之上，包括一种新颖的冗余校正 MALF 技术，该技术在过去两年中引发了细分的巨大挑战。目标 1 寻求通过用更快、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 中最耗时的组件）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，而且还会使其对解剖变异性更加鲁棒，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少图像处理专业知识的用户在桌面上充分利用 MALF 分割。 Aim 3 还将提供一组公开可用的地图集，以及用户根据自己的数据构建新的自定义地图集集的方法。 Aim 4 将对新方法和软件进行广泛评估，以挑战现实世界的临床成像数据，包括大脑和心脏成像。作为本次评估的一部分，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。