KiTS19 - Kidney Tumor Segmentation Challenge 2019
KiTS19 is part of the MICCAI 2019 Challenge.
The goal of this challenge is to accelerate the development of reliable kidney and kidney tumor semantic segmentation methodologies.
Requirements
- Python >= 3.6
- PyTorch >= 1.0.0
pip install -r requirements.txt
Getting Started
1. Download kits19 Dataset
Make sure to install git-lfs before cloning!
Clone kits19 repository (~54 GB)
git clone https://github.com/neheller/kits19.git
2. Conversion data
Conversion nii.gz to npy for easy to read slice (~140 GB)
python conversion_data.py -d "kits19/data" -o "data"
3. Train ResUNet for Coarse Kidney Segmentation
python train_res_unet.py -e 100 -b 32 -l 0.0001 -g 4 -s 512 512 -d "data" --log "runs/ResUNet" --eval_intvl 5 --cp_intvl 5 --vis_intvl 0 --num_workers 8
4. Capture Coarse Kidney ROI
python get_roi.py -b 32 -g 4 -s 512 512 --org_data "kits19/data" --data "data" -r "runs/ResUNet/checkpoint/best.pth" -o "data/roi.json"
5. Train DenseUNet for Kidney Tumor Segmentation
python train_dense_unet.py -e 100 -b 32 -l 0.0001 -g 4 -s 512 512 -d "data" --log "runs/DenseUNet" --eval_intvl 5 --cp_intvl 5 --vis_intvl 0 --num_workers 8
6. Evaluation Test Case
python eval_dense_unet.py -b 32 -g 4 -s 512 512 -d "data" -r "runs/DenseUNet/checkpoint/best.pth" --vis_intvl 0 --num_workers 8 -o "out"
7. Post-processing
python post_processing.py -d "out" -o "out_proc"
We are the 21st of total 106 teams.
TODO