Development And Applications Of The Open Microscopy Environment (OME)

近年来，我们一直专注于OME分析系统并开发强大的通用图像分析方法，最终形成了我们的模式识别工具WND-CHRM。我们已经使用从荧光显微镜到人体膝盖X光的各种成像方式验证了这种用于生物图像分析的模式识别方法。我们还验证了一系列应用程序，从基于图像的评分分析到疾病诊断再到未来疾病风险预测。报告AG000674-10和AG000685-07介绍了这一办法的具体应用。 最近的一项主要工作是重写WND-Charm代码库，使其更加模块化、组织更好、更易于使用，并可通过Python脚本语言进行访问。最近发布的WND-Charm1.50可以从我们的代码库(https://code.google.com/p/wnd-charm/)，)获得，它具有显著的速度改进以及几个错误修复。最近，为将WND-Charm与Omero集成做出了重大努力，导致在2014年8月发布了集成项目的开发人员预览版。此外，加州大学圣巴巴拉分校的Bisque开发团队已经能够在他们自己的开源软件项目中使用我们修改后的代码来进行图像特征计算。正在努力完成对WND-Charm代码库的重新组织。 全图像分析已被证明非常有用，但并不总是可以相互比较整个图像。相对均匀的图像的例子是培养的细胞或组织，如肌肉、肝脏和某些类型的肿瘤。我们在人体膝关节X光片上的工作(见AG000685-07)是第一个需要对不同受试者的图像进行一定程度的预处理以进行相互比较的应用。在本例中，我们只需在每个图像中找到膝关节的中心，并为所有患者提取围绕该中心的固定半径。在具有复杂解剖结构的图像中存在一个复杂得多的对齐问题。这方面最极端的例子可能是大脑组织的染色切片。对准问题的解决方案将允许使用广义模式识别来解决解剖学背景下的形态差异。 空间分辨模式分析给我们软件的性能带来了极大的负担。为了获得空间分辨率，不是一次考虑整个图像，也不是将其分割成网格上的少量瓷砖，而是必须对每个图像进行数千或数百万次采样。为了使这种类型的应用程序实用，必须重新考虑软件中使用的计算策略。以前，计算每个图像样本的全部3000个低级别图像特征，即使其中大多数后来被发现与分类问题无关，因为它们缺乏辨别能力。实现空间分辨模式识别的策略的主要变化是消除了不必要的计算。这需要针对图像特征的按需计算策略，这是wndchrm软件的主要架构目标。我们当前的版本使用了这一策略，提供了一个非常简单易用的API，用于指定要计算的特定特征。 我们还使大多数底层C++代码可以从Python脚本语言访问，以便更容易地定制如何在新应用程序中使用WND-Charm。现在可以使用Python接口计算按需特性，并使用可用于Python(NumPy、Scipy)的数学和科学计算库执行进一步处理。我们的公共代码库(https://github.com/wnd-charm/wnd-charm).)上公开提供了与PYTHON相关的软件 2012年，与Jason Swedlow(苏格兰邓迪大学)合作，启动了一个由Wellcome Trust赞助的大型国际项目，以开发OME/Omero系统的具体应用。我们的团队对这个项目的贡献包括提供Omero和WND-Charm之间的接口，以便在大型、多样化的图像存储库中进行图像比较。最终目标是使用模式识别来自动注释添加到这些集合中的新图像，基于先前注释的图像和大量在背景中自主操作的独立分类器。像OME/Omero这样的系统的主要设计目标是为科学家提供组织和注释他们的图像集合的简单方法。图像的组织结构及其按注释进行的分组也可以作为训练模式识别分类器的主要输入。由于分类器只需要很少或不需要用户的额外输入，这两种技术的自然融合代表了一种强大的新模式，可以最大限度地利用大规模科学和医学图像数据库。目前，我们有一个与Omero交互的功能原型，它可以读取图像数据和注释；使用这些数据和注释来训练分类器；并将分类得到的注释返回给Omero。 要使这一综合系统切实可行，还有大量的工作要做。当前的开发者预览版在Omero方面缺乏灵活性，无法跟踪多个潜在冲突的图像分类，以及维护灵活的图像特征集存储。此外，Omero和Python-WND-Charm软件包都需要对子图像区域(ROI)和系统图像平铺方案提供更好的支持。尽管有这些限制，但发布的软件能够训练分类器，并使用它来对以前未见过的图像进行分类。

In recent years, we've focused on the OME analysis system and developing robust general image analysis methodology, culminating in our pattern recognition tool called WND-CHRM. We have validated this pattern-recognition approach to biological image analysis using diverse imaging modalities ranging from fluorescence microscopy to X-rays of human knees. We have also validated a range of applications from scoring image-based assays to diagnosis of disease to prediction of future disease risk. The specific applications of this approach are covered in reports AG000674-10 and AG000685-07. A major effort recently has been to rewrite the WND-CHARM code-base to make it more modular, better organized, easier to use, and accessible with the Python scripting language. A recent release of WND-CHARM 1.50 is available from our code repository (https://code.google.com/p/wnd-charm/), with significant speed improvements as well as several bug fixes. Most recently, a significant effort was made to integrate WND-CHARM with OMERO, resulting in a developer preview release of the integrated project in August 2014. Additionally, the Bisque development team at UC-Santa Barbara in BS Manjunath's group has been able to use our revamped code for image feature calculation in their own open-source software project. Efforts are to ongoing to finish reorganizing the WND-CHARM codebase. Whole-image analysis has proven very useful, but it is not always possible to compare whole images to each other. Examples of relatively homogenous images are those of cultured cells, or tissues like muscle, liver, and certain types of tumors. Our work on human knee X-Rays (see AG000685-07) was the first application where a certain degree of pre-processing was necessary to align images of different subjects to compare them to each other. In this case, we simply found the center of the knee joint in each image and extracted a fixed radius around this center for all patients. A much more complicated alignment problem exists in images with complex anatomy. Possibly the most extreme example of this are stained sections of brain tissue. A solution to the alignment problem would allow the use of generalized pattern recognition to address morphological differences in an anatomical context. Spatially-resolved pattern analysis places an extreme burden on the performance of our software. Instead of an entire image being considered at once, or split into a small number of tiles on a grid, to achieve spatial resolution, each image must be sampled thousands or millions of times. In order to make this type of application practical, the computational strategy used in the software must be reconsidered. Previously, all 3,000 low-level image features were calculated for each image sample, even when most of them were later found to be irrelevant to the classification problem because they lacked discrimination power. The major change in strategy to enable spatially-resolved pattern recognition is to eliminate unnecessary calculations. This requires an on-demand computing strategy for image features, which is a major architectural goal for the wndchrm software. Our current release makes use of this strategy, exposing a very simple to use API for specifying the specific features to be computed. We have have also made the majority of the underlying C++ code accessible from the Python scripting language to make it easier to customize how WND-CHARM is used in new applications. It is now possible to compute on-demand features using the Python interface and perform further processing using mathematical and scientific computing libraries available for Python (numpy, scipy). The Python-related software is publicly available on our public code repository (https://github.com/wnd-charm/wnd-charm). In 2012, in collaboration with Jason Swedlow (University of Dundee, Scotland), a large international project sponsored by the Wellcome Trust was initiated to develop specific applications of the OME/OMERO system. Our group's contribution to this project involves providing interfaces between OMERO and WND-CHARM to enable image comparisons in large, diverse image repositories. The eventual goal is to use pattern recognition to annotate new images added to these collections automatically, based on previously annotated images and a large set of independent classifiers that opeerate autonomously in the background. The primary design goal of a system like OME/OMERO is to provide scientists with easy ways of organizing and annotating their image collections. The organizational structure of the images and their grouping by their annotations can also serve as the primary inputs for training pattern-recognition classifiers. Because classifiers require little or no additional input from the user, the natural convergence of these two technologies represent a powerful new mode for maximizing the utility of large scale scientific and medical image databases. Currently we have a functioning prototype that interacts with OMERO to read image data and annotations; use these for training a classifier; and return annotations derived from classification back to OMERO. Substantial work remains to make this integrated system practical. The current developer-preview release lacks flexibility on the OMERO side to keep track of multiple, potentially conflicting classifications for images, as well as maintain a flexible store of image feature sets. Additionally, both the OMERO and the Python-WND-CHARM software packages need better support for sub-image regions (ROIs) and systematic image tiling schemes. Despite these limitations, the released software is capable of training a classifier and using it to classify previously unseen images.