This is the Git repositiory for the program 'CNN assisted PSF localization (CAPL)' based on the paper Neural Network assisted localization of clustered point spread functions in Single Molecule Localization Microscopy
The program was developed in Python 3.8.8. Later versions of python should be supported but not been tested yet. If you are on a later version, you may give it a try!
I would suggest you to use Anaconda / Miniconda and set up a virtual environment as:
- conda create -n "myenv" python=3.8.8 # replace "myenv" with your desired name.
After setting up the virtual environemnt, you can install the dependencies as:
-
pip install -r requirements.txt
-
Additionally you will need to set up Fiji / ImageJ and install the ThunderSTORM plugin.
The program is divided into 4 parts:
- 01_training_data_generation.ipynb : this notebok generates the training files required to train the model.
- 02_training_model.ipynb : this notebok trains the CNN model
- 03_prediction.ipynb : this notebok is used to employ the trained model to predict super-resolved images from unseen data
- ImageVisualization.py : this script is reused from one of my old projects; used to create the super-resolved image from the detections
Step by step procedure to use each notebook is incorcorated in the notebooks itself.
This code is prepared based on Deep-STORM and ZeroCostDL4Mic platform. Please also cite their work.
Thank you,
Pranjal Choudhury