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