rocaseg - Robust Cartilage Segmentation from MRI

Source code for Panfilov et al. "Improving Robustness of Deep Learning Based Knee MRI Segmentation: Mixup and Adversarial Domain Adaptation", https://arxiv.org/abs/1908.04126v3.

Important!

The camera-ready version contained a bug in Dice score computation for tibial cartilage on Dataset C. Please, refer to the arXiv version for the corrected values - https://arxiv.org/abs/1908.04126v3.

Description

1. To reproduce the experiments from the article one needs to have access to OAI iMorphics, OKOA, and MAKNEE datasets.

2. Download code from this repository.

3. Create a fresh Conda environment using environment.yml. Install the downloaded code as a Python module.

4. datasets/prepare_dataset_... files show how the raw data is converted into the format supported by the training and the inference pipelines.

5. The structure of the project has to be as follows:

   ./project/
       | ./data_raw/  # raw scans and annotations
            | ./OAI_iMorphics_scans/
            | ./OAI_iMorphics_annotations/
            | ./OKOA/
            | ./MAKNEE/
       | ./data/  # preprocessed scans and annotations
       | ./src/ (this repository)
       | ./results/  # models' weights, intermediate and final results 
            | ./0_baseline/
                 | ./weights/
                 | ...
            | ./1_mixup/
            | ./2_mixup_nowd/
            | ./3_uda1/
            | ./4_uda2/
            | ./5_uda1_mixup_nowd/
   

6. File scripts/runner.sh contains the complete description of the workflow.

7. Statistical testing is implemented in notebooks/Statistical_tests.ipynb.

8. Pretrained models are available at https://drive.google.com/open?id=1f-gZ2wCf55OVjgA8oXd7xttGVW5DUUcU .

Legal aspects

This code is freely available only for research purposes.

The software has not been certified as a medical device and, therefore, must not be used for diagnostic purposes.

Commercial use of the provided code and the pre-trained models is strictly prohibited, since they were developed using the medical datasets under restrictive licenses.

Cite this work

@InProceedings{Panfilov_2019_ICCV_Workshops,
  author = {Panfilov, Egor and Tiulpin, Aleksei and Klein, Stefan and Nieminen, Miika T. and Saarakkala, Simo},
  title = {Improving Robustness of Deep Learning Based Knee MRI Segmentation: Mixup and Adversarial Domain Adaptation},
  booktitle = {The IEEE International Conference on Computer Vision (ICCV) Workshops},
  month = {Oct},
  year = {2019}
}