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Title: A rapid and efficient 2D/3D nuclear segmentation method for analysis of early mouse embryo and stem cell image data
Authors: Lou, Xinghua
Kang, Minjung
Xenopoulos, Panagiotis
Muñoz-Descalzo, Silvia 
Hadjantonakis, Anna Katerina
UNESCO Clasification: 32 Ciencias médicas
3201 Ciencias clínicas
Keywords: Embryo
Embryonic Stem Cells
Image processing
Issue Date: 2014
Journal: Stem Cell Reports 
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.
ISSN: 2213-6711
DOI: 10.1016/j.stemcr.2014.01.010
Source: Stem Cell Reports[ISSN 2213-6711],v. 2, p. 382-397 (Marzo 2014)
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