Software

Here you can find all the software related to bioimaging where Fernando Amat has participated. All the packages include some sort of documetation, source code and references to the paper describing the methodlogies. If you have any question, comment or suggestion do not hesitate to send an email to This e-mail address is being protected from spambots. You need JavaScript enabled to view it

You can find other projects and pieces of code in my Bitbucket repository.



Automatic cell lineage reconstruction PDF Print E-mail

The comprehensive reconstruction of cell lineages in complex multicellular organisms is a central goal of developmental biology. We present an open-source computational framework for segmentation and tracking of cell nuclei with high accuracy and speed. We demonstrate its (1) generality, by reconstructing cell lineages in four-dimensional, terabyte-sized image data of fruit-fly, zebrafish and mouse embryos, acquired with three different types of fluorescence microscopes, (2) scalability, by analyzing advanced stages of development with up to 20,000 cells per time point, at 26,000 cells min-1 on a single computer workstation, and (3) ease of use, by adjusting only two parameters across all data sets and providing visualization and editing tools for efficient data curation. Our approach achieves on average 97.0% linkage accuracy across all species and imaging modalities.

 

You can download the source code here or in the Keller Lab website on the software section.

 

Please, if you use this software in your research cite the following paper:

 

[1] Amat, F., Lemon, W., Mossing, D., McDole, K., Wan, Y., Branson, K. et al. (2014). Fast, accurate reconstruction of cell lineages from large-scale fluorescence microscopy data. Nature Methods, 11(9), 951-958.

Last Updated on Thursday, 06 November 2014 03:03
 
Fast and robust optical flow for time-lapse microscopy using super-voxels PDF Print E-mail

Segmentation and tracking are two of the main computational tasks required to extract relevant biological information from many light microscopy recordings. Optical flow estimation has been used as a key module in many segmentation and tracking systems and is considered a mature technology in the field of computer vision. However, most of the research has focused on two-dimensional (2D) natural images, which are small in size and rich in edges and texture information. In contrast, three-dimensional (3D) time-lapse recordings of biological specimens can comprise up to several terabytes of image data and often exhibit complex dynamic behaviors as well as object blurring due to the point spread function of the microscope. Thus, new approaches to optical flow are required to improve performance for such data.

 

We solve optical flow in large three-dimensional (3D)+time microscopy datasets by defining a Markov Random Field (MRF) over super-voxels in the foreground of each volume and applying motion smoothness constraints between each super-voxel instead of voxel-wise. Super-voxels improve registration in textureless areas, the MRF over super-voxels efficiently propagates motion information between neighboring cells, and the background subtraction and super-voxels reduce the dimensionality of the problem by an order of magnitude. We validate our approach on large 3D+time-lapse data of Drosophila and zebrafish development by analysing cell motion patterns. We show that our new approach is on average 10x faster than the commonly used optical flow implementations in the Insight ToolKit (ITK) and 23% more accurate.

 

You can download the source code here

 

Please, if you use this software in your research cite the following paper:

 

[1]F. Amat, E. W. Myers, and P. J. Keller, “Fast and robust optical flow for time-lapse microscopy using super-voxels,” Bioinformatics, vol. 29, no. 3, pp. 373–380, Feb. 2013.

Last Updated on Friday, 08 November 2013 20:48
 
RAPTOR PDF Print E-mail

Robust Alignment and Projection Estimation for Tomographic Reconstruction  (RAPTOR) is a free available software to align raw stacks obtained from electron microscopes for tomographic purposes.  It is intended to automatically obtain a full-precision alignment comparable to the one obtained with extended manual intervention. Fiducial particles are needed in the image for the alignment. It uses a probabilistic framework to deal with very noisy images like in thick cryo EM datasets.  More information can be find in [1].  The main idea is to automate alignment, so users don’t have to spend too much time selecting gold particles.

The code has been compiled for linux machines for 32 or 64 bit machines.  It is completely written in C++ and it is self contained. All the needed libraries are included in the tar file. The tar file also includes a README file with instructions on how to use and compile RAPTOR from command line. 

· Raptor for Linux and Mac OS

Update: RAPTOR is now included as part of IMOD distribution, so it works for all platforms (including Windows) and can be called as a stand alone application from terminal command line or from within the eTomo user interface.

Raptor has been tested in many different datasets (cryo-EM, plastic, thick sections…) and different geometries and has given very good results. So far, the best results have been obtained in cryo. We continue working to improve results. Any feedback, question or suggestion will be greatly appreciated. It is the only way to improve things:) This software is intended for research purposes. Any commercial application that intends to use Raptor should contact the authors first. Please, if you use Raptor in your research cite the following paper:

 

[1] F. Amat, F. Moussavi, L. R. Comolli, G. Elidan, K. H. Downing, and M. Horowitz, “Markov random field based automatic image alignment for electron tomography,” Journal of Structural Biology, vol. 161, no. 3, pp. 260-275, Mar. 2008.
Last Updated on Friday, 22 November 2013 14:25
 
Thresholded Constrained cross-correlation PDF Print E-mail

In the past few years, three-dimenional (3D) subtomogram classification and averaging has become an important tool in cryo-electron tomography (CET). This technique allows to resolve higher resolution structures of targets that can not be reconstructed by single-particle methods. Based on previous approaches, we present a new dissimilarity measure between subtomograms named Thresholded Constrained Cross-Correlation (TCCC). TCCC has shown to improve results for Signal-to-Noise Ratios (SNR) lower than 0.1. This allows to analyze macromolecules in thicker samples like whole cell or lower the defocus in thinner samples to push the first zero of the Contrast Transfer Function (CTF). TCCC uses statistics of the noise to automatically select only a small percentage of the Fourier coefficients to compute the cross-correlation. The thresholding has two main advantages in the cross-correlation score: first, reduces the influence of the noise; second, avoids the missing wedge normalization problem since we consider the same amount of coefficients for all possible pairs of subtomograms. You can download the code to test TCCC here:

· Parallel TCCC for Linux

The code also incorporates the possibility to use Constratined Cross-Correlation (Forster et al. JSB 2008) and the dissimilarity score from (Bartesaghi et al. JSB 2008). The code has been compiled for linux machines for 32 or 64 bit machines.  It is completely written in C++ and it only needs the FFTW libraries compiled for floating point precision. The tar file also includes a README file with instructions on how to use and compile the code from command line. It also includes an example folder to show how to use the code. Finally, it you are brave you can test the code in MacOS. There is no reason why it should not compile, so if it does, please let me know and I update MacOS to supported platforms.

This software is intended for research purposes. Any commercial application that intends to use TCCC should contact the authors first. Please, if you use TCCC in your research cite the following paper:

[1] F. Amat, L. R. Comolli, F. Moussavi, J. Smit, K. H. Downing, and M. Horowitz, “Subtomogram alignment by adaptive Fourier coefficient thresholding,” Journal of Structural Biology, vol. 171, no. 3, pp. 332-344, Sep. 2010.
Last Updated on Friday, 22 November 2013 14:26