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: Cell number changes during normal development, and in disease (e.g., neurodegeneration, cancer). Many genes affect cell number, thus functional genetic analysis frequently requires analysis of cell number alterations upon loss of function mutations or in gain of function experiments. Drosophila is a most powerful model organism to investigate the function of genes involved in development or disease in vivo. Image processing and pattern recognition techniques can be used to extract information from microscopy images to quantify automatically distinct cellular features, but these methods are still not very extended in this model organism. Thus cellular quantification is often carried out manually, which is laborious, tedious, error prone or humanly unfeasible. Here, we present DeadEasy Mito-Glia, an image processing method to count automatically the number of mitotic cells labelled with anti-phospho-histone H3 and of glial cells labelled with anti-Repo in Drosophila embryos. This programme belongs to the DeadEasy suite of which we have previously developed versions to count apoptotic cells and neuronal nuclei. Having separate programmes is paramount for accuracy. DeadEasy Mito-Glia is very easy to use, fast, objective and very accurate when counting dividing cells and glial cells labelled with a nuclear marker. Although this method has been validated for Drosophila embryos, we provide an interactive window for biologists to easily extend its application to other nuclear markers and other sample types. DeadEasy MitoGlia is freely available as an ImageJ plug-in, it increases the repertoire of tools for in vivo genetic analysis, and it will be of interest to a broad community of developmental, cancer and neuro-biologists.
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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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