Commit 46b44803 authored by Andrew Cohen's avatar Andrew Cohen

updates to readme.md

parent 9ff2511e
# **LEVER**
### The Lineage Editing and Validation tool
LEVER is a MATLAB tool for cell segmentation, tracking and lineaging. By default LEVER tries to identify neural stem cells in phase contrast images. However, the segmentation and tracking algorithms can be extended to identify other cell types using different image modalities. Additional information on extending LEVER can be found in the LEVER [wiki](https://git-bioimage.coe.drexel.edu/opensource/lever/wikis/home).
LEVER is a collection of software tools for analyzing the development of proliferating (dividing) cells in time-lapes microscopy image sequences. LEVER includes algorithms for segmentation, tracking and lineaging. LEVER also includes a user interface that allows the segmentation, tracking and lineaging results to be *validated*, with any errors in the automated processing easily identified and corrected.
#### Related Publications
LEVER has been applied to the analysis of thousands of neural progentior cells (NPC) across hundreds of clones. The NPC analysis results along with a discussion of our LEVER algorithms and CloneView web visualization tool was published in Stem Cell Reports.
#### Using LEVER for your imaging application
M. Winter, M. Liu, D. Monteleone, J. Melunis, U. Hershberg, S. K. Goderie, S. Temple, and A. R. Cohen, _Computational Image Analysis Reveals Intrinsic Multigenerational Differences Between Anterior and Posterior Cerebral Cortex Neural Progenitor Cells_, Stem Cell Reports, 2015. http://dx.doi.org/10.1016/j.stemcr.2015.08.002
The key to analyzing time-lapse microscopy images of live proliferating cells is the *segmentation algorithm*. Given perfect segmentation and sufficient temporal resolution of imaging, the tracking and lineaging algorithms will never make a mistake. Sadly, (or happily, depending on your perspective) perfect segmentation will never exist. The entire LEVER architecture is designed to use temporal data from the tracking and contextual information on spatio-temporal population dynamics from the lineaging to improve the performance of the segmentation algorithm.
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The original LEVER software protocol with usage instructions for analyzing proliferating cells was published in Nature Protocols.
The version of LEVER available here includes segmentation algorithms for adult and embryonic mouse neural stem cells imaged with phase contrast microscopy. There are as yet un-released segmentation algorithms for phase/brightfield and FUCCI images of human cancer cells and mouse T cells. There are also versions of LEVER that include 3-D visualization and segmentation for 5-D (3-D+time+channels) fluorescence microscopy including confocal, multi-photon and lattice light sheet. These algorithms will generally be released concurrent with the publication of the related manuscript(s). We are always interested in developing new collaborations - please contact acohen 'at' coe.drexel.edu if you are interested in working with us to develop a LEVER segmentation for your application.
Winter et al., _Vertebrate neural stem cell segmentation, tracking and lineaging with validation and editing_, Nat Protocols, vol. 6, pp. 1942-1952, 2011.
Additional information on LEVER can be found in the LEVER [wiki](https://git-bioimage.coe.drexel.edu/opensource/lever/wikis/home). This includes sections on using LEVER, and on extending the program with custom segmentation algorithms.
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LEVER uses an integratedtracking algorithm termed Multitemporal Association Tracking (MAT). The algorithm, applied to microtubule transport tracking was originally published in the International Journal of Computational Biology and Drug Design. A comparison of MAT and other particle tracking algorithms was subsequently published in Nature Methods.
#### Referencing LEVER
LEVER may be cited using:
Chenouard et al., _Objective comparison of particle tracking methods_, Nat Methods, Jan 19 2014.
* M. Winter, M. Liu, D. Monteleone, J. Melunis, U. Hershberg, S. K. Goderie, S. Temple, and A. R. Cohen, _Computational Image Analysis Reveals Intrinsic Multigenerational Differences Between Anterior and Posterior Cerebral Cortex Neural Progenitor Cells_, Stem Cell Reports, 2015. http://dx.doi.org/10.1016/j.stemcr.2015.08.002 [pubmed](http://www.ncbi.nlm.nih.gov/pubmed/26344906).
Winter et al., _Axonal transport analysis using Multitemporal Association Tracking_, International Journal of Computational Biology and Drug Design, vol. 5, pp. 35-48, 2012.
* Winter et al., _Vertebrate neural stem cell segmentation, tracking and lineaging with validation and editing_, Nat Protocols, vol. 6, pp. 1942-1952, 2011. [pubmed](http://www.ncbi.nlm.nih.gov/pubmed/22094730).
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LEVER was developed at Drexel University's Bioimaging lab under the direction of Dr. Andrew Cohen. For more information check the lab homepage http://bioimage.coe.drexel.edu.
Additional LEVER publications include
* Mankowski, W. C., Winter, M. R., Wait, E., et al., _Segmentation of occluded hematopoietic stem cells from tracking_, Conf Proc IEEE Eng Med Biol Soc, vol. 2014, pp. 5510-3, 2014.[pubmed](http://www.ncbi.nlm.nih.gov/pubmed/25571242).
* Wait, E., Winter, M., Bjornsson, C., et al., _Visualization and correction of automated segmentation, tracking and lineaging from 5-D stem cell image sequences_, BMC Bioinformatics, vol. 15, 2014. [pubmed](http://www.ncbi.nlm.nih.gov/pubmed/25281197).
LEVER uses an integrated tracking algorithm called Multitemporal Association Tracking (MAT). The algorithm, applied to tracking axonal organelle transport was originally published in the International Journal of Computational Biology and Drug Design. This is the best reference on MAT. A comparison of MAT and other particle tracking algorithms was subsequently published in Nature Methods. The same MAT algorithm, consisting of just a few hundred lines of C code has been applied to dozens of applications in cell and organelle tracking.
* Winter et al., _Axonal transport analysis using Multitemporal Association Tracking_, International Journal of Computational Biology and Drug Design, vol. 5, pp. 35-48, 2012. [pubmed](http://www.ncbi.nlm.nih.gov/pubmed/22436297).
* Chenouard et al., _Objective comparison of particle tracking methods_, Nat Methods, Jan 19 2014, [pubmed](http://www.ncbi.nlm.nih.gov/pubmed/24441936).
## Get The Source Code
......@@ -65,7 +72,7 @@ General usage as well as specific interface documentation are linked below:
Further information on LEVER is available on the bioimage/LEVER wiki at https://git-bioimage.coe.drexel.edu/opensource/lever/wikis/home
## License
Copyright 2015 Andrew Cohen
Copyright (c) 2011-2016 Andrew Cohen
LEVer is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
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