Adaptive Large-Scale Framework for Automatic Biomedical Image Segmentation

描述(申请人提供)：多图谱标记融合(MARF)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。它可以说是有史以来开发的最成功的通用自动图像分割技术。在医学成像的临床和研究应用中，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步骤，而且手动和半自动方法不适合今天日益庞大和复杂的成像数据集。尽管有多篇论文展示了MARF方法在一系列生物医学成像应用中的卓越表现，但更广泛的生物医学成像研究社区采用这项技术的速度一直很慢。这可以由多种因素解释，包括该技术的高计算要求，缺乏交钥匙软件实现，以及在临床成像数据集和广泛的病理学存在的情况下缺乏验证。本申请旨在消除这些障碍，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MARF领域创新的强劲记录基础上，包括一种新颖的冗余纠正MARF技术，该技术在过去两年中导致了分割的重大挑战。目标1试图通过用更快且约束更少的稀疏配准策略来取代密集可变形图像配准来提高马尔可夫随机场的计算性能。我们假设，这不仅将降低MARF的计算成本，而且还将使其对解剖变异更稳健，特别是能够将其用于肿瘤和病变分割。AIM 2提出了对MARF的算法扩展，支持动态和多模式成像数据集的自动分割，这在MARF文献中被很大程度上忽视了。Aim 3将开发MARF方法的交钥匙开源实现。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MARF分割。AIM 3还将提供一套公开可用的地图集，并使用户能够根据自己的数据建立新的定制地图集。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。作为这项评估的一部分，我们将量化我们的MARF方法和竞争技术在多大程度上适用于采集参数和临床表型不同的新型成像数据集。

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.