Retistruct

-- computational reconstruction and transformation of flattened retinae

Retistruct is an R package to morph a flat surface with cuts (a dissected flat-mount retina) onto a curvilinear surface (the a standard retinal shape). It can estimate the position of a point on the intact adult retina to within 8° of arc (3.6% of nasotemporal axis). The coordinates in reconstructed retinae can be transformed to visuotopic coordinates.

How Retistruct works

Reconstruction is achieved by: stitching the marked-up cuts of the flat-mount outline; dividing the stitched outline into a mesh whose vertices then are mapped onto a curtailed sphere; and finally moving the vertices so as to minimise a physically-inspired deformation energy function.

Installation and documentation

Retistruct has been tested on GNU/Linux (Ubuntu 12.04) and Mac OS X 10.8, and Microsoft Windows Vista. (Note that as of 30th October 2013, it is not possible to install the graphical user interface on MacOS X 10.9 (Mavericks). This is due to the RGtk2 package upon which the Retistruct GUI depends.) Instructions on how to install the latest version and use Retistruct are contained in the User Guide. The installation contains a number of demonstration retinae, and instructions for how to handle retinal flat-mount images in Retistruct.

For reference purposes, this zip file contains the review version of Retistruct and some Matlab code to read data directories contained by Retistruct.

Access to the source code Subversion repository is available from the project page.

Sample data

As well as the built-in demo data, there are some sample images to practise on:

  1. Beginner: SMI-32 stained retina. As described in the User Guide, the outline can be marked up in ImageJ and imported into Retistruct.
  2. More advanced: TIFF files (left and right), each containing a stack of three images corresponding to Figure 6 of the manuscript: retinae labelled with Fluoro-Emerald, Fluoro-Ruby and a brightfield image. As described in the User Guide, use ImageJ to mark up the outline on the brightfield image, and then use ImageJ's particle analyser to find the locations of the stained cells.

Authors and funding

Retistruct was written by David Sterratt at the University of Edinburgh, and tested by Daniel Lyngholm and Ian Thompson at the MRC Centre for Developmental Neurobiology, KCL.

This work was supported by a Programme Grant from the Wellcome Trust (G083305).