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Automatic Cone Photoreceptor Localisation with MDRNNs

This repo contains an implementation of the method described in this paper. Please cite the paper if you use the code.

@Article{Davidson2018,
    author={Davidson, Benjamin
    and Kalitzeos, Angelos
    and Carroll, Joseph
    and Dubra, Alfredo
    and Ourselin, Sebastien
    and Michaelides, Michel
    and Bergeles, Christos},
    title={Automatic Cone Photoreceptor Localisation in Healthy and Stargardt Afflicted Retinas Using Deep Learning},
    journal={Scientific Reports},
    year={2018},
    volume={8},
    number={1},
    pages={7911},
    issn={2045-2322},
    doi={10.1038/s41598-018-26350-3},
    url={https://doi.org/10.1038/s41598-018-26350-3}
}

Getting Started

To install and use requires:

  • Python 3.5.x or 3.6.x
  • pip

Installing

  1. Download the git repository to a folder of your choice, /path/to/code/ConeDetector

  2. Install Python package using pip. Ubuntu: pip install /path/to/code/ConeDetector; Windowspython -m pip install /path/to/code/ConeDetector

    • If you do not have a gpu, pip install tensorflow: Ubuntupip install tensorflow; Windowspython -m pip install tensorflow
    • If you do have a gpu, follow these instructions to install tensorflow-gpu

If you just want to apply the model from the paper, you only need tensorflow, not tensorflow-gpu. The gpu version is needed if you want to train new models in any reassonable amount of time.

Using

  • Any images should be of the form, where xxxx is a number with leading zeros, eg 1==0001
INITIAL_XXXX_WHATEVER.tif
  • The required lut.csv for applying models should be of the following form, if we have two subjects, for example, with a um to pixel of 0.76 and 0.85 respectively.
    INITIAL_0001, 0.76
    INITIAL_0002, 0.85
  • To run the code open a cmd prompt, or terminal and enter:
cone_detector

Features

After running cone_detector from a terminal a gui will launch asking what you want to do.

Apply existing models

  • Required: folder of tifs, lut.csv for each subject in folder
  • Applies model to tifs to estimate locations
  • Can simply trust the algorithm, or manually correct each image
  • Outputs locations and stats for each image

Build training data sets for training new models

  • Required: folder of tifs
  • Create labeled data in format used by tensorflow to train new models
  • Can select a model to aid the annotations, or do completely by hand
  • Will save data set as tfrecord, to train new models

Train new models

  • Required: training data set built using cone_detector
  • Required: a validation data set created using cone_detector
  • Will run same training regime described in the paper
  • Saves new model, which can be applied in cone_detector