A Flexible Framework for Web Interfaces to Image Databases: Supporting User-Defined Ontologies and Links to External Databases

An increasing number of free software tools have been made available for the evaluation of fluorescence cell micrographs. The main users are biologists and related life scientists with no or little knowledge of image processing. In this review, we give an overview of available tools and guidelines about which tools the users should use to segment fluorescence micrographs. We selected 15 free tools and divided them into stand-alone, Matlab-based, ImageJ-based, free demo versions of commercial tools and data sharing tools. The review consists of two parts: First, we developed a criteria catalogue and rated the tools regarding structural requirements, functionality (flexibility, segmentation and image processing filters) and usability (documentation, data management, usability and visualization). Second, we performed an image processing case study with four representative fluorescence micrograph segmentation tasks with figure-ground and cell separation. The tools display a wide range of functionality and usability. In the image processing case study, we were able to perform figure-ground separation in all micrographs using mainly thresholding. Cell separation was not possible with most of the tools, because cell separation methods are provided only by a subset of the tools and are difficult to parametrize and to use. Most important is that the usability matches the functionality of a tool. To be usable, specialized tools with less functionality need to fulfill less usability criteria, whereas multipurpose tools need a well-structured menu and intuitive graphical user interface.

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“…(). It provides image processing, segmentation with scripts (Johnston et al ., ) and a web interface for manual segmentation.…”

Section: Methodsmentioning

confidence: 99%

Review of free software tools for image analysis of fluorescence cell micrographs

Wiesmann

Franz

Held

et al. 2014

Journal of Microscopy

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“…Another important open source platform is Open Microscopy Environment (OME) 5 [73,116,138], which is a software package and a set of standards for image informatics —the collection, maintenance, and analysis of biological images and the associated data. The aim of this system is to standardize how image information is stored, extracted and transported among different software applications, including both commercial and non-commercial products.…”

Section: Histology Image Processing and Analysismentioning

confidence: 99%

“…OMEDS stores all meta-data and the derived knowledge about the images in a database. These servers are accessed using a web user interface via a Java API (web client), or by using a plugin for ImageJ (Java client) [73]. The major task of OME is not to create novel image analysis algorithms, but instead to develop of a structure that allows applications to access and use any data associated with, or generated from, digital microscope images.…”

Section: Histology Image Processing and Analysismentioning

confidence: 99%

Histology image analysis for carcinoma detection and grading

Long

Antani

et al. 2012

Computer Methods and Programs in Biomedicine

307

184

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This paper presents an overview of the image analysis techniques in the domain of histopathology, specifically, for the objective of automated carcinoma detection and classification. As in other biomedical imaging areas such as radiology, many computer assisted diagnosis (CAD) systems have been implemented to aid histopathologists and clinicians in cancer diagnosis and research, which have been attempted to significantly reduce the labor and subjectivity of traditional manual intervention with histology images. The task of automated histology image analysis is usually not simple due to the unique characteristics of histology imaging, including the variability in image preparation techniques, clinical interpretation protocols, and the complex structures and very large size of the images themselves. In this paper we discuss those characteristics, provide relevant background information about slide preparation and interpretation, and review the application of digital image processing techniques to the field of histology image analysis. In particular, emphasis is given to state-of-the-art image segmentation methods for feature extraction and disease classification. Four major carcinomas of cervix, prostate, breast, and lung are selected to illustrate the functions and capabilities of existing CAD systems.

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“…Related approaches Several general image processing methods including CellProfiler [13], Icy [14], OMERO [15], ImageJ [16], CellTracker [17] and ImageM [18] have been developed for the quantitative analysis of images, and the capabilities of each software are described in Table 1. OMERO is a platform for the storage and annotation of microscopy images [15], and Icy and ImageJ have been developed as general platforms for image analysis [14,16]. All three are dependent on the development of plugins for specific applications from its user-base.…”

Section: Ifish: Immunofluorescence In Situ Hybridizationmentioning

confidence: 99%

GoIFISH: a system for the quantification of single cell heterogeneity from IFISH images

et al. 2014

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Molecular analysis has revealed extensive intra-tumor heterogeneity in human cancer samples, but cannot identify cell-to-cell variations within the tissue microenvironment. In contrast, in situ analysis can identify genetic aberrations in phenotypically defined cell subpopulations while preserving tissue-context specificity. GoIFISH is a widely applicable, user-friendly system tailored for the objective and semi-automated visualization, detection and quantification of genomic alterations and protein expression obtained from fluorescence in situ analysis. In a sample set of HER2-positive breast cancers GoIFISH is highly robust in visual analysis and its accuracy compares favorably to other leading image analysis methods. GoIFISH is freely available at www.sourceforge.net/projects/goifish/. Quantifying cell-to-cell heterogeneity in the tissue contextIntra-tumor heterogeneity is currently accepted as a hallmark of cancer, being present in virtually all tumor traits [1]. Sensitive molecular techniques developed in the last few years have allowed a detailed genetic and phenotypic deconvolution of intra-tumor heterogeneity. These include genome-wide analysis of bulk tumor samples to describe evolutionary trajectories in relapsed tumors and genomic divergence between primary tumors and metastases [2][3][4], as well as single-cell genomic profiling [2,5]. However, despite methodological improvements in the molecular characterization of single cells, the accurate interpretation of intra-tumor heterogeneity requires the inference of cell-to-cell variability within a particular tissue context, which can only be directly assessed by in situ analysis. Microenvironmental constraints within spatially restricted areas of a tumor can exert differential selective pressures, leading to the manifestation and the selection of different phenotypes and particular genotypes. For instance, different oxygen levels, the presence the development of objective analytical tools that minimize scoring subjectivity and facilitate the quantification of multiple traits in single cells while preserving context specificity. The implementation of these tools in both basic and translational research will advance our understanding of tumor biology and will facilitate biomarker discovery and validation.GoIFISH: quantifying tumor heterogeneity in IFISH images For application to IFISH, accurate segmentation at the nuclear, membrane and spot level are critical for subsequent analysis, which often interrogates clonal populations or evaluates relationships between protein and genomic expression. Objective integration of protein expression and copy number requires not only accurate segmentation, but also the separation of normal cells from tumor cells, and appropriate background subtraction associated with auto-fluorescence. Very few existing softwares allow manual alterations of small inaccuracies in cell segmentation and often incorrect cell classification results cannot be changed. Visual scoring by a trained observer (e.g. pathologist) is the gold...

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A Flexible Framework for Web Interfaces to Image Databases: Supporting User-Defined Ontologies and Links to External Databases

Cited by 10 publications

References 3 publications

Review of free software tools for image analysis of fluorescence cell micrographs

Review of free software tools for image analysis of fluorescence cell micrographs

Histology image analysis for carcinoma detection and grading

GoIFISH: a system for the quantification of single cell heterogeneity from IFISH images

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