Monte Carlo Dropout Network (MCDN) 3D CT Segmentation

A Tensorflow and Keras backed framework for learned segmentation methods of 3D CT scan volumes. Supported functionality includes training models, running inference and quantifying uncertainty. The main underlying model architecture is V-Net.

Installation

mcdn-3d-seg expects Python >=3.5. Also, be sure to activate your desired virtual environment before installing.

Run one of the following to install, depending on if you want GPU support:

# for GPU support
pip install -e .[gpu]

# for CPU only
pip install -e .[cpu]

Sacred Configuration

The JSON file sacred_config.json specifies sacred experiment configuration independent of the run configuration.

Specifically, file_observer_base_dir specifies where sacred stores its run logs. The default is mcdn-3d-seg/runs/.

Run Configuration

mcdn-3d-seg contains multiple scripts such as train.py and infer.py, each of which expects a JSON config. The syntax to run one of these scripts is:

python <PATH/TO/SCRIPT>.py with <PATH/TO/CONFIG>.json

Creating a JSON config

The default config is a python dict, DEFAULT_CONFIG, in the file ctseg/config.py. The JSON config needs to match the syntax of DEFAULT_CONFIG but only needs to contain override values.

1. create an empty JSON file
2. add individual fields from DEFAULT_CONFIG or copy/paste the entire config into your JSON file
3. modify values as appropriate

Important: be sure to update the inputs and outputs as the default values are just placeholders.

Training

Important config parameters:

• normalization: the default is no normalization but you will likely want to change this
• train_config, test_config: the inputs and outputs here need to be specified for every experiment.

Note: inference_config and plot_config are not used during training.

Once a config has been created, use the config to train via:

python train.py with <PATH/TO/CONFIG>.json

Resuming Training

1. Select whether you want to resume from the best model or the latest model. If you saved the entire model (default), then use resume_from; otherwise use load_weights_from
2. Resume training the model:

python train.py with <PATH/TO/CONFIG>.json resume_from=<"best" OR "latest">`

Inference

Before running inference, be sure you have specified the correct input/output paths in inference_config of the JSON config. Once set, run inference via:

python infer.py with <PATH/TO/CONFIG>.json

To run inference using a different model, add resume_from=<PATH/TO/MODEL> at the end of the above command.

Authors

Tyler Ganter; Carianne Martinez; Kevin Potter; Jessica Jones; Tyler LaBonte