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.
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 language | English |
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| DOIs | |
| Publication status | Published - 2014 |