Automatic segmentation of high-throughput RNAi fluorescent cellular images

    Authors

    P. K. Yan; X. B. Zhou; M. Shah;S. T. C. Wong

    Comments

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    Abbreviated Journal Title

    IEEE T. Inf. Technol. Biomed.

    Keywords

    fluorescent microscopy; high throughput; image segmentation; interaction; model; level set; multichannel; LEVEL SET APPROACH; ACTIVE CONTOURS; CELLS; MICROSCOPY; ALGORITHMS; TRACKING; NUCLEI; MODEL; Computer Science, Information Systems; Computer Science, ; Interdisciplinary Applications; Mathematical & Computational Biology; Medical Informatics

    Abstract

    High-throughput genome-wide RNA interference (RNAi) screening is emerging as an essential tool to assist biologists in understanding complex cellular processes. The large number of images produced in each study make manual analysis intractable; hence, automatic cellular image analysis becomes an urgent need, where segmentation is the first and one of the most important steps. In this paper, a fully automatic method for segmentation of cells from genome-wide RNAi screening images is proposed. Nuclei are first extracted from the DNA channel by using a modified watershed algorithm. Cells are then extracted by modeling the interaction between them as well as combining both gradient and region information in the Actin and Rac channels. A new energy functional is formulated based on a novel interaction model for segmenting tightly clustered cells with significant intensity variance and specific phenotypes. The energy functional is minimized by using a multiphase level set method, which leads to a highly effective cell segmentation method. Promising experimental results demonstrate that automatic segmentation of high-throughput genome-wide multichannel screening can be achieved by using the proposed method, which may also be extended to other multichannel image segmentation problems.

    Journal Title

    Ieee Transactions on Information Technology in Biomedicine

    Volume

    12

    Issue/Number

    1

    Publication Date

    1-1-2008

    Document Type

    Article

    Language

    English

    First Page

    109

    Last Page

    117

    WOS Identifier

    WOS:000252517900014

    ISSN

    1089-7771

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