A rapid and efficient 2D/3D nuclear segmentation method for analysis of early mouse embryo and stem cell image data Journal Article


Authors: Lou, X.; Kang, M.; Xenopoulos, P.; Munoz Descalzo, S.; Hadjantonakis, A. K.
Article Title: A rapid and efficient 2D/3D nuclear segmentation method for analysis of early mouse embryo and stem cell image data
Abstract: Segmentation is a fundamental problem that dominates the success of microscopic image analysis. In almost 25 years of cell detection software development, there is still no single piece of commercial software that works well in practice when applied to early mouse embryo or stem cell image data. To address this need, we developed MINS (modular interactive nuclear segmentation) as a MATLAB/C++-based segmentation tool tailored for counting cells and fluorescent intensity measurements of 2D and 3D image data. Our aim was to develop a tool that is accurate and efficient yet straightforward and user friendly. The MINS pipeline comprises three major cascaded modules: detection, segmentation, and cell position classification. An extensive evaluation of MINS on both 2D and 3D images, and comparison to related tools, reveals improvements in segmentation accuracy and usability. Thus, its accuracy and ease of use will allow MINS to be implemented for routine single-cell-level image analyses. © 2014 The Authors.
Keywords: controlled study; nonhuman; animal cell; mouse; image analysis; embryo; embryonic stem cell; computer interface; computer program; mouse embryo; blastocyst; kernel method; single cell analysis; measurement accuracy; priority journal; article; cell counting; embryo segmentation; modular interactive nuclear segmentation
Journal Title: Stem Cell Reports
Volume: 2
Issue: 3
ISSN: 2213-6711
Publisher: Cell Press  
Date Published: 2014-03-01
Start Page: 382
End Page: 397
Language: English
DOI: 10.1016/j.stemcr.2014.01.010
PROVIDER: scopus
PMCID: PMC3964288
PUBMED: 24672759
DOI/URL:
Notes: Cited By (since 1996):1 -- Export Date: 1 August 2014 -- Source: Scopus
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  1. Xinghua Lou
    7 Lou
  2. Min Jung Kang
    12 Kang