Maqingyang/RibFrac

Usage

Install Required Packages

First install required packages in requirements.txt using pip:

pip install -r requirements.txt

Training

To train the FracNet model, run the following in command line:

python -m main --train_image_dir <training_image_directory> --train_label_dir <training_label_directory> --val_image_dir <validation_image_directory> --val_label_dir <validation_label_directory>

Prediction

To generate prediction, run the following in command line:

python -m predict --image_dir <image_directory> --pred_dir <predition_directory> --model_path <model_weight_path>

In the predict.py, we adopt a post-processing procedure of removing low-probability regions, spine regions, and small objects. This procedure leads to fewer false negatives. You may also skip the post-processing by setting --postprocess False in the command line argument and check the raw output.

Evaluation

To evaluate your prediction, run the following in command line:

python -m ribfrac.evaluation --gt_dir <gt_directory> -pred_dir <prediction_directory> --clf False