MINS is a a Matlab based software tool for automated segmentation and quantitation of cell nuclei from various microscopic imagery.

- Very simple to use
- Works for 2d and 3d images
- Accurate and efficient nuclear detection and segmentation
- Automated separation of multiple embryos
Automated outlier (debris, imaging artifacts, etc.)
- Automated outlier (debris, imaging artifacts, etc.) removal
- Classification of ICM/TE cells
- It's FREE

Graphical User Interface:

Overview of the MINS platform. (A) The main graphical user interface (GUI) of MINS. The top boxes contain functions for parameter loading and saving. The middle boxes correspond to the entire processing pipeline. The bottom boxes allow batch processing on a large number of datasets. (B) The processing pipeline and the output of each modules. (C) Detailed outputs ease any downstream analyses, either manually or by integration with other software tools. Overlay of segmentation and raw data allows rapid and straightforward inspection of the results. A segmentation information summary provides easy access to quantitation results.

* Click the image to enlarge

Result Preview (Flash player required)

1. Segmentation of 2D Sequence
* Dataset from DCellIQ package
* Note: Each frame is segmented independently. Each cell nucleus is randomly assigned with a unique color. Results are presented frame by frame.

2. Segmentation of 3D Volume
* Dataset from Silvia Muñoz Descalzo
* Note: The entire volume is segmented jointly. Each cell nucleus is randomly assigned with a unique color. The result is presented slice by slice.

3. Embryo Separation and Outlier Removal
* Top row: outlier removal on 3D data with false detection
* Bottom row: embryo separation and outlier removal for 3D data (five embryos, many false positives due to background noise)
* Click the image to enlarge

Related Publications:
If you find MINS useful, please cite this paper:
* Lou, X., Kang, M., Xenopoulos, P., Muñoz-Descalzo, S., and Hadjantonakis, A.-K. A rapid and efficient 2D/3D segmentation method for analysis of early mouse embryo and stem cell image data. In Stem Cell Reports, Volume 2, Issue 3, 382-397, 2014.

Other papers that used MINS:
* Chia Le Bin, G., Muñoz-Descalzo, S., Kurowski, A., Leitch, H., Lou, X., Mansfield, W., Charles-Etienne, D., Grabole, N., Mulas, C., Niwa, H., Hadjantonakis, A.-K. and Nichols, J. (2014) Oct4 is required for lineage priming in the developing inner cell mass of the mouse blastocyst. Development 141:1001-10. 
* Schrode, N., Saiz, N., Di Talia, S. and Hadjantonakis, A.-K. GATA6 levels modulate primitive endoderm cell fate choice and timing in the mouse blastocyst. (2014) Developmental Cell 29:454-67. 2014 
* Xenopoulos, P., Kang, M., Puliafito, A., Di talia, S. and Hadjantonakis, A.-K. Heterogeneities in Nanog Expression Drive Stable Commitment to Pluripotency in the Mouse Blastocyst. (2015) Cell Reports S2211-1247(15)00138-2.

    * Only load the first image of a stacked .tiff files (3D data)

1. Q: When trying to load data, the program returns an error saying "Undefined function 'bioimread' for input arguments of type 'char'." 
A: MINS loads its components (such as bioimread) using a relative path so you must start the program when in the root of MINS. So, after launching your Matlab, using the following commands:
Step 1. cd [your MINS root]
Explanation: This sets the current operating path of your Matlab to the MINS root path
Step 2. startMINS
Explanation: This launches MINS