Vessel-Captcha: An Efficient Learning Framework for Vessel Annotation and Segmentation

This is the implementation of our Vessel-CAPTCHA framework for vessel brain MRI segmentation in Keras.

1. Preprocessing Data

Skull stripping:

Removing the skull from the MRI images by applying the mask.

python -m captcha.preprocessing.skull_stripping --original_data_dir <path-to-raw-dataset> --target_dir <path-to-skull-stripping-dir>

2. Generating Weak Annotations

Grid creating:

Creating the grid for weak annotation.

python -m captcha.preprocessing.grid --original_data <path-to-raw-dataset> --grid_filepath <path-to-grid-dir>

Rough-mask creating:

python -m captcha.preprocessing.rough_mask --patch_size 32 --clustering 'kmeans' --patch_annotation_dir <path-to-patch-annotation-dir> --rough_mask_dirh <path-to-rough-mask-dir>

3. Building a Training Dataset

Create a directory for image patches utilized for training.

2D-PnetCl:

python -m captcha.preprocessing.cls_patch_extraction --patch_size 32  --skull_stripping_dir <path-to-skull-stripping-volume-contain-patch-annotation-file> --patch_vessel_dir <path-to-extracted-patches>

2D-WnetSeg:

python -m captcha.preprocessing.seg_patch_extraction --skull_stripping_dir <path-to-skull-stripping-volume-contain-pixelwise-weak-annotation> --patch_extraction_dir <path-to-extracted-patches>

4. Training Models

2D-PnetCl:

We perform training a classification model using the following command:

python -m captcha.train_pnetcls --model_arch 'pnetcls' --patch_dir <path-to-extracted-patches>  --train_metadata_filepath <path-to-save-metadata> --model_filepath <path-to-save model>

Data Augmentation for 2D-WnetSeg (optional):

python -m captcha.pnetcls.prediction --patch_size 32 --train_non_label_dir <path-to-non-label-data> --train_metadata_filepath <path-to-load-metadata> --model_filepath <path-to-load-pnetcl-model> --grid_label_filepath <path-to-save-patch-label>

2D-WnetSeg:

We perform training a segmentation model using the following command:

python -m captcha.train_wnetseg --label_rough_dir <path-to-extracted-patches> --model_arch  'wnetseg' --train_metadata_filepath <path-to-save-metadata> --model_filepath <path-to-save-model> --patch_size 96

5. Vessel Segmentation

We are now able to classify pixels with and without vessel from the brain MRI volume using the trained segmentation model from the previous step. The command that helps us to do this is:

python -m captcha.predict_full_testset --test_set_dir <path-to-testset> --model_arch 'wnetseg' --patch_size 96 --train_metadata_filepath <path-to-load-metadata> --model_filepath <path-to-load-wnetseg-model> --prediction_filepath <patch-to-save-vessel-segmentation>

Applying filtering (optional):

python -m captcha.filter --patch_size 32 --wnetseg_prediction_dir <path-to-testset> --train_metadata_filepath <path-to-load-metadata> --model_filepath <path-to-load-pnetcl-model> --wnetseg_pnetcl_filepath <path-to-save-vessel-segmentation>

6. Evaluating Framework's Performance

python -m captcha.evaluation_segmentation --result_dir <path-to-prediction-dir>