In this repository we provide the code base that accompanies the paper:

https://doi.org/10.1371/journal.pone.0258546

The content and structure of the repo is given by the following:

.
├── README.md
├── brevis
│   ├── data                : python scripts for loading the datasets required
│   ├── loss                : for loss functions not included in PyTorch
│   ├── model               : python scripts for the various neural networks
│   ├── test                : scripts requied when running the models in test mode
│   ├── train               : scripts requied when running the models in train mode
│   └── utils               : utility functions (e.g. for converting the images to numpy arrays for faster data loading)
│
├── configs                 : .json files for running the models to reconstruct the three fluorescence channels
│                       
└── exp_stats               : .csv files for image statistics and train/test splits 
    

The data should be structured as:

.
|--"adipocyte_data"       :Base folder where images are stored
|   |-- "60x images"      :What magnification the images are images at
    |-- "masks"           :where the nuclei masks are stored
        |-- "60x images"
            |-- "Nuclei"

The image files in the magnification folder are named as ex AssayPlate_Greiner_#655090_{well}_T0001F0{FOV}L01A0{Action_nr}Z0{stack_nr}C0{channel}.tif where "well" and "FOV" are imported from exp_stats. "Action_nr" range from 1-4, stack_nr range from 1-7 for bright-field images and channel is one of C01 (Nuclei), C02 (Lipid droplets), C03 (Cytoplasm), C04 (Bright-field)

To train the models, modify the content in the config file that you want to run. For instance change the path to the data under "data" - "folder" and the training validation and test splits csv files under "data" -- ""train_csv_file", "validation_csv_file" and "test_csv_file". You can also specify other things such as model configurations, optimizers, learning rate ...

To start training execute for instance for nuclei channel:

python3 -m brevis -c configs/nuclei_train.json 

To run the pre-trained models on your own data, edit the config for the respective channel and put the "run_mode" to "test" and the path to the model in "model_path" in "test". The data should be formatted in the same way as the mentioned on Data above. Then, to run the inference simply run the same command:

python3 -m brevis -c configs/nuclei_train.json