Automated Processing of Imaging Data through Multi-tiered Classification of Biological Structures Illustrated Using Caenorhabditis elegans.
Automated Processing of Imaging Data through Multi-tiered Classification of Biological Structures Illustrated Using Caenorhabditis elegans.
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DOI：
10.1371/journal.pcbi.1004194
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发表时间：
2015-04
影响因子：
4.3
通讯作者：
Lu H
中科院分区：
文献类型：
--
作者：
Zhan M;Crane MM;Entchev EV;Caballero A;Fernandes de Abreu DA;Ch'ng Q;Lu H
定量成像已成为生物发现和临床诊断中的重要技术;最近开发了大量工具，以实现新的和加速的生物调查形式。新的成像方式、对比技术、显微镜工具、微流体和计算机控制系统所提供的高通量实验能力越来越多地将实验瓶颈从物理操作和原始数据收集的水平转移到自动识别和数据处理。然而，尽管它们具有广泛的重要性，但满足这些需求的图像分析解决方案的量身定制范围很窄。在这里，我们提出了一个可推广的配方自主识别特定的生物结构，适用于许多问题。我们在这里提出的流程架构利用标准的图像处理技术和多层应用的分类模型，如支持向量机（SVM）。这些低级功能在大量的图像处理软件包和编程语言中很容易获得。因此，我们的框架是很容易实现的模块化水平，并提供了具体的高层次架构，以指导解决更复杂的图像处理问题。我们展示了实用的分类程序，通过开发两个特定的分类器作为自动化和细胞识别的工具集，在模式生物秀丽隐杆线虫。为了满足C. elegans研究社区，我们贡献了一个现成的分类器，用于在明场成像下识别动物的头部。此外，我们扩展了我们的框架，以解决荧光成像下的细胞特异性识别的普遍问题，这是在多细胞生物或组织的生物学研究的关键。使用这些例子作为指导，我们设想了广泛的效用的框架，不同的长度尺度和成像方法的各种问题。新技术增加了生物成像数据集的规模和内容丰富性。因此，自动化图像处理越来越有必要以客观、一致和省时的方式提取相关数据。虽然图像处理工具已被开发的一般问题，影响大社区的生物学家，生物研究问题和实验技术的多样性留下了许多问题没有解决。此外，没有明确的方法可以让非计算机科学家立即应用大量的计算机视觉和图像处理技术来解决他们的特定问题或调整现有的工具以满足他们的需求。在这里，我们通过展示一个适应性强的图像处理框架来满足这一需求，该框架能够以高精度和高计算效率来适应大范围的生物学问题。此外，我们证明了利用这个框架的不同的问题，通过解决两个特定的图像处理的挑战，在模式生物秀丽隐杆线虫。除了对C。elegans社区，这里开发的解决方案为其他生物问题提供了有用的概念和适应性强的图像处理模块。
Quantitative imaging has become a vital technique in biological discovery and clinical diagnostics; a plethora of tools have recently been developed to enable new and accelerated forms of biological investigation. Increasingly, the capacity for high-throughput experimentation provided by new imaging modalities, contrast techniques, microscopy tools, microfluidics and computer controlled systems shifts the experimental bottleneck from the level of physical manipulation and raw data collection to automated recognition and data processing. Yet, despite their broad importance, image analysis solutions to address these needs have been narrowly tailored. Here, we present a generalizable formulation for autonomous identification of specific biological structures that is applicable for many problems. The process flow architecture we present here utilizes standard image processing techniques and the multi-tiered application of classification models such as support vector machines (SVM). These low-level functions are readily available in a large array of image processing software packages and programming languages. Our framework is thus both easy to implement at the modular level and provides specific high-level architecture to guide the solution of more complicated image-processing problems. We demonstrate the utility of the classification routine by developing two specific classifiers as a toolset for automation and cell identification in the model organism Caenorhabditis elegans. To serve a common need for automated high-resolution imaging and behavior applications in the C. elegans research community, we contribute a ready-to-use classifier for the identification of the head of the animal under bright field imaging. Furthermore, we extend our framework to address the pervasive problem of cell-specific identification under fluorescent imaging, which is critical for biological investigation in multicellular organisms or tissues. Using these examples as a guide, we envision the broad utility of the framework for diverse problems across different length scales and imaging methods. New technologies have increased the size and content-richness of biological imaging datasets. As a result, automated image processing is increasingly necessary to extract relevant data in an objective, consistent and time-efficient manner. While image processing tools have been developed for general problems that affect large communities of biologists, the diversity of biological research questions and experimental techniques have left many problems unaddressed. Moreover, there is no clear way in which non-computer scientists can immediately apply a large body of computer vision and image processing techniques to address their specific problems or adapt existing tools to their needs. Here, we address this need by demonstrating an adaptable framework for image processing that is capable of accommodating a large range of biological problems with both high accuracy and computational efficiency. Moreover, we demonstrate the utilization of this framework for disparate problems by solving two specific image processing challenges in the model organism Caenorhabditis elegans. In addition to contributions to the C. elegans community, the solutions developed here provide both useful concepts and adaptable image-processing modules for other biological problems.
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影响因子：
13.9
作者：
Fenno L;Yizhar O;Deisseroth K
通讯作者：
Deisseroth K
影响因子：
12.3
作者：
Carpenter AE;Jones TR;Lamprecht MR;Clarke C;Kang IH;Friman O;Guertin DA;Chang JH;Lindquist RA;Moffat J;Golland P;Sabatini DM
通讯作者：
Sabatini DM
影响因子：
48
作者：
Eliceiri, Kevin W.;Berthold, Michael R.;Goldberg, Ilya G.;Ibanez, Luis;Manjunath, B. S.;Martone, Maryann E.;Murphy, Robert F.;Peng, Hanchuan;Plant, Anne L.;Roysam, Badrinath;Stuurmann, Nico;Swedlow, Jason R.;Tomancak, Pavel;Carpenter, Anne E.
通讯作者：
Carpenter, Anne E.
影响因子：
3.7
作者：
Johnston, Josiah;Iser, Wendy B.;Chow, David K.;Goldberg, Ilya G.;Wolkow, Catherine A.
通讯作者：
Wolkow, Catherine A.
DOI：
10.1109/34.917571
发表时间：
2001-04-01
影响因子：
23.6
作者：
Mohan, A;Papageorgiou, C;Poggio, T
通讯作者：
Poggio, T