A Rapid and Efficient 2D/3D Nuclear Segmentation Method for Analysis of Early Mouse Embryo and Stem Cell Image Data

作     者：Lou, Xinghua Kang, Minjung Xenopoulos, Panagiotis Munoz-Descalzo, Silvia Hadjantonakis, Anna-Katerina 

作者机构：Sloan Kettering Inst Dev Biol Program New York NY 10065 USA Cornell Univ Dept Biochem Cell & Mol Biol Program Weill Grad Sch Med Sci New York NY 10065 USA 

出 版 物：《STEM CELL REPORTS》 (Stem Cell Rep.)

年 卷 期：2014年第2卷第3期

页      面：382-397页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 0710[理学-生物学] 07[理学] 071009[理学-细胞生物学] 09[农学] 0901[农学-作物学] 090102[农学-作物遗传育种] 

基　　金：Human Frontiers Sciences Program National Institutes of Health [RO1-HD052115, RO-1DK084391] NYSTEM 

主　　题：Algorithms Animals Embryo, Mammalian/cytology Embryo, Mammalian/metabolism Embryonic Stem Cells/cytology Embryonic Stem Cells/metabolism Image Processing, Computer-Assisted/methods Imaging, Three-Dimensional/methods Mice Microscopy/methods Software 

摘      要：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.