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

Xinghua Lou, Minjung Kang, Panagiotis Xenopoulos, S Munoz-Descalzo, Anna-Katerina Hadjantonakis

Research output: Contribution to conferencePaper

55 Citations (Scopus)
142 Downloads (Pure)

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.
Original languageEnglish
DOIs
Publication statusPublished - 2014

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