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Long-Range-3D-Self-Attention-for-Prostate-Segmentation

Model and dataset from the paper Long-Range 3D Self-Attention for Prostate Segmentation

Installation

Use the package manager pip to install requirements.

pip install -r requirements.txt

Usage

Our proposed model can be trained with the command:

main.py --dataset public_prostata --interp_size 256 
--crop_size 56 144 144 --crop_type center --loss BCE_jaccard --learning_rate 0.1 --job_id ####_##

To load weights of a given experiment you can specify its job id with the keyword --job_id ####_## Predictions and ground truth slices can be dump by using the flag --plot_flag

Dataset

We used the Prostate-MRI-US-Biopsy after a lot of pre-processing. We included two YAML file inside the preprocessing folder so that researchers can replicate our dataset easily:

  1. prostate_stl.yml contains all the selected patients along with their label as STL files;

  2. prostate_npy.yml contains all the selected patients along with their label as npy files;

you can use the former to create the numpy files from meshes using the function in preprocessing/explore_dataset.py, you can then use the latter to feed our dataloader. Remember to update the path with your numpy_yaml in yaml_segmentation_dataset.py.

we also included other useful functions in preprocessing/explore_dataset.py to check the quality of MRI scans.

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