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PCL_CB2_stellating 1ea726edb7 | 1 year ago | |
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data | 1 year ago | |
dataset | 1 year ago | |
loss | 1 year ago | |
network | 1 year ago | |
res | 1 year ago | |
utils | 1 year ago | |
.gitignore | 1 year ago | |
LICENSE.md | 1 year ago | |
README.md | 1 year ago | |
conversion_data.py | 1 year ago | |
eval_dense_unet.py | 1 year ago | |
get_roi.py | 1 year ago | |
post_processing.py | 1 year ago | |
requirements.txt | 1 year ago | |
train_dense_unet.py | 1 year ago | |
train_res_unet.py | 1 year ago |
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.
pip install -r requirements.txt
Make sure to install git-lfs before cloning!
Clone kits19 repository (~54 GB)
git clone https://github.com/neheller/kits19.git
Conversion nii.gz to npy for easy to read slice (~140 GB)
python conversion_data.py -d "kits19/data" -o "data"
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
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"
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
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"
python post_processing.py -d "out" -o "out_proc"
We are the 21st of total 106 teams.
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