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Flow based consistencies for self-supervised vessel segmentation

This is the code for the MICCAI 2021 paper Self-Supervised Vessel Enhancement Using Flow-Based Consistencies.

Framework

Installation

Install the packages from requirements.txt file using pip.

pip install -r requirements.txt

Training

To run each of these scripts, go to the src directory.

DRIVE/STARE dataset

To train a model to predict vesselness (without considering bifurcations) use the following script:

python train.py -c configs/v1/drivesq4_1110.json --run_id DRIVE

Other configurations like configs/v1/drivesq4_1101.json, configs/v1/drivesq4_10.110.json, etc. can also be used. Refer to the configuration files in configs/v1 for more configurations (or create your own!).

For inference of the trained model on DRIVE/STARE dataset, use the following script:

python test.py -c configs/v1/drivesq4_1110.json --run_id DRIVE_test --resume /path/to/ckpt.pth --train [1/0]
python test.py -c configs/v1/starefinetune.json --run_id STARE_test --resume /path/to/ckpt.pth --train [1/0]

The --train parameter specifies if you wish to save the vesselness files for training or test set. This is important because the training set is used to determine the threshold, and then evaluation is done with this threshold on the test set.


Bifurcations

To train a model to predict vesselness (considering bifurcations) use the following script:

python train.py -c configs/bifurcv1/drivesq4bifurconly_1110.json --run_id DRIVE_bifurc

For inference of the trained model, use the same configuration file (since the configuration files specify the loss function and vesselness functions to use).

python test.py -c configs/bifurcv1/drivesq4bifurconly_1110.json --run_id DRIVE_bifurc_test --resume /path/to/ckpt.pth --train [1/0]

RITE/HRF dataset

Similar to DRIVE dataset, you can use the following script for training:

python train.py -c configs/rite/ritebifurc.json --run_id RITE
python train.py -c configs/hrf/hrfbifurc.json --run_id HRF

To test the method, use the following scripts

python test.py -c configs/rite/ritebifurc.json --run_id RITE_test --path /path/to/ckpt.pth --train [1/0]
python test.py -c configs/hrf/hrfbifurc.json --run_id HRF_test --path /path/to/ckpt.pth --train [1/0]

3D datasets

To train a model to predict vesselness in 3D datasets like COPDGene, VESSEL12, VascuSynth and TubeTK datasets, use the following scripts:

python train.py -c configs/copd/copdsq4.json --run_id COPDGene
python train.py -c configs/vessel12/vessel12bifurc.json --run_id VESSEL12 
python train.py -c configs/tubetk/mrabifurc.json --run_id TubeTK
python train.py -c configs/vascu/vascusigma0.json --run_id VascuSynth

Testing is also similar to the 2D datasets:

python testcopd3d.py -c configs/copd/copdsq4.json --run_id COPDGene_test --resume /path/to/ckpt.pth --patientIDs <list of patientIDs>
python test3d.py -c configs/vessel12/vessel12bifurc.json --run_id VESSEL12_test --resume /path/to/ckpt.pth
python test3d.py -c configs/tubetk/mrabifurc.json --run_id TubeTK_test --resume /path/to/ckpt.pth
python test3d.py -c configs/vascu/vascusigma0.json --run_id VascuSynth_test --resume /path/to/ckpt.pth

The COPD test also takes a list of patient IDs because there are over 9000 patient volumes.

For 3D datasets, we also save results from other baselines (Frangi, Sato, etc.):

python testbaselines_vessel12.py --config configs/vessel12/vessel12bifurc.json --vesselfunc <method> --batch_size 40 --num_cores 16
python testbaselines_tubetk.py --config configs/tubetk/mrabifurc.json --vesselfunc <method> --batch_size 40 --num_cores 16

Evaluate metrics

2D datasets (DRIVE/STARE/RITE/HRF)

When test.py runs, it asks for a path to save the vesselness and other outputs. Specify a filename, and save it. Now, use this saved model to get evaluation metrics. Use the following script:

python get_all_metrics_drivestare.py -c configs/v1/drivesq4_1110.json --run_id DRIVE_test --resume /path/to/ckpt.pth --method <method>
python get_all_metrics_[rite|hrf].py -c /path/to/training_config.json --run_id [RITE|HRF]_test --resume /path/to/ckpt.pth --method <method>

Use the --method parameter to specify which vesselness method to use for evaluation (choices: frangi, sato, hessian, meijering, ours).


Reference

If you use our work in your research or wish to refer to the results in our paper, please use the following BibTex reference:

@article{jena2021self,
  title={Self-supervised vessel enhancement using flow-based consistencies},
  author={Jena, Rohit and Singla, Sumedha and Batmanghelich, Kayhan},
  journal={arXiv preprint arXiv:2101.05145},
  year={2021}
}

TODO

  • Add paths to pretrained models.
  • File names need to be changed at a lot of places (preferably use a master config file for storing common items like data and save directories, etc.).

About

This is the code for the MICCAI 2021 paper Self-Supervised Vessel Enhancement Using Flow-Based Consistencies.

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