This process gives files vsi come from Olympus cellSens Software. Each file contains a sequence of color images.
In order to analyze video we have used the Stardis method (https://github.com/stardist/stardist/).
> Uwe Schmidt, Martin Weigert, Coleman Broaddus, and Gene Myers. [Cell Detection with Star-convex Polygons](https://arxiv.org/abs/1806.03535). _International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)_, Granada, Spain, September 2018.
StarDist is a deep-learning-based method of 2D and 3D nucleus detection from Martin Weigert and Uwe Schmidt. StarDist is powered by a deep learning model that is trained to detect specific kinds of nuclei.
For our study, we annotated a database of images and built a model specific to our problem. We searched for chromatophores and classified them in 2 categories: dark and light.
The set of chromatophores were drawn on 29 images of size 1544x1038 pixels from 9 different acquisition sequences. The 29 images were cut into pieces of 256x256 pixels for the creation of the model giving 696 thumbnails. On all the images, this corresponds to 25919 chromatophores classified as "light" and 26819 chromatophores classified as "dark".
The programs to build the model and use it as well as our current model are available on this gitlab.
The presentation of the project can be found on the wiki page: https://git.unicaen.fr/nicolas.elie/redpol-open/-/wikis/home
# Installation
@@ -41,13 +24,14 @@ The project is mainly based on the Stardist method: https://github.com/stardist/
The proposed installation uses Conda environment: https://docs.conda.io/en/latest/.
You can install an environment via the provided environment file(s): \_filename.\_yml.
