Conference Paper

Determination of Mitotic Delays in 3D Fluorescence Microscopy Images of Human Cells Using an Error-Correcting Finite State Machine.

DOI: 10.1007/978-3-540-71091-2_49 Conference: Bildverarbeitung für die Medizin 2007, Algorithmen, Systeme, Anwendungen, Proceedings des Workshops vom 25.-27. März 2007 in München
Source: DBLP

ABSTRACT In high-throughput cell phenotype screens large amounts of image data are acquired. The evaluation of these microscopy images re- quires automated image analysis methods. Here we introduce a compu- tational scheme to process 3D multi-cell image sequences as they are produced in large-scale RNAi experiments. We describe an approach to automatically segment, track, and classify cell nuclei into seven difierent mitotic phases. In particular, we present an algorithm based on a flnite state machine to check the consistency of the resulting sequence of mi- totic phases and to correct classiflcation errors. Our approach enables automated determination of the duration of the single phases and thus the identiflcation of cell cultures with delayed mitotic progression.

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    ABSTRACT: Tracking and registration approaches have been developed for automatic analysis of multidimensional biomedical images. The tracking approach allows computing the trajectories of cells in fluorescence microscopy image sequences. The registration approach enables to geometrically align cell microscopy images by using elastic transformations.
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    ABSTRACT: In high-throughput RNAi knockdown screens large amounts of image data are acquired. The evaluation of these microscopy images constitutes a bottleneck and motivates the devel- opment of automated image analysis methods. This contribution is concerned with the au- tomated evaluation of RNAi knockdown experiments for studying delays in mitotic phases. To this end, 3D multi-cell image sequences of living cell nuclei are acquired. Based on these images, the duration of the mitotic phases has to be measured for the treated cells and compared with the normal cells from control experiments. To automatically determine the lengths of the cell cycle phases, we have developed a workflow that comprises segmentation, tracking of splitting nuclei, extraction of static and dynamic features, classification, and phase length determination. For fast and accurate segmentation we use a region adaptive thresholding technique on the maximum intensity projected images (Fig. 1a,b). We perform tracking of the splitting cell nuclei using a two step approach. First, correspondences are determined by exploit- ing the smoothness of potential trajectories. Second, mitosis events are detected based on morphological properties and the corresponding trajectories are merged (Fig. 1c). Based on the tracking result we automatically select the most informative slice for each nucleus from the 3D image, which is then used for feature extraction. Besides static image features, we additionally include dynamic image features which represent temporal changes of the cell morphology between ancestrally related cells. A support vector machine classifier is used to classify the nuclei into the following seven cell cycle phases: Interphase, Prophase, Prometaphase, Metaphase, Anaphase1, Anaphase2, and Telophase (Fig. 2). Finally, we have
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    ABSTRACT: Gene perturbation experiments in combination with fluorescence time-lapse cell imaging are a powerful tool in reverse genetics. High content applications require tools for the automated processing of the large amounts of data. These tools include in general several image processing steps, the extraction of morphological descriptors, and the grouping of cells into phenotype classes according to their descriptors. This phenotyping can be applied in a supervised or an unsupervised manner. Unsupervised methods are suitable for the discovery of formerly unknown phenotypes, which are expected to occur in high-throughput RNAi time-lapse screens. We developed an unsupervised phenotyping approach based on Hidden Markov Models (HMMs) with multivariate Gaussian emissions for the detection of knockdown-specific phenotypes in RNAi time-lapse movies. The automated detection of abnormal cell morphologies allows us to assign a phenotypic fingerprint to each gene knockdown. By applying our method to the Mitocheck database, we show that a phenotypic fingerprint is indicative of a gene's function. Our fully unsupervised HMM-based phenotyping is able to automatically identify cell morphologies that are specific for a certain knockdown. Beyond the identification of genes whose knockdown affects cell morphology, phenotypic fingerprints can be used to find modules of functionally related genes.
    BMC Bioinformatics 10/2013; 14(1):292. DOI:10.1186/1471-2105-14-292 · 2.67 Impact Factor

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