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kits19_3d_segmentation

Requirements

  • Ubuntu OS (tested with Ubuntu 20.04 LTS)
  • NVIDIA Driver (tested with 455.32.00)
  • Docker (tested with 19.03.12)
  • Docker Compose (tested with 1.27.4)
  • NVIDIA Container Tookit (tested with 2.4.0)

May work with other versions.

Setup

Prepare the container:

docker-compose run --rm --service-ports kits19 bash

All the commands below have to be executed in this container.

Prepare KiTS19 Dataset

Download

Download KiTS19 dataset:

./scripts/download_kits19.sh

This script downloads the KiTS19 dataset under $HOME/data/kits19/data on your host machine (host $HOME/data is mounted to container /data).

Resample

Apply re-sampling with a fixed spacing to the images:

python tools/resample_kits19.py

This script saves the re-sampled images and labels under $HOME/data/kits19_preprocessed on your host machine.

Make train/val folds

Split case ids into N folds (N=5 at default) randomly:

python tools/make_folds.py

This script saves train_*.json and val_*.json under $HOME/data/kits19_preprocessed on your host machine.

Training

Get your API key from W&B and then:

# replace xxxx with your own W&B API key
echo 'WANDB_API_KEY = "xxxx"' > .env

# train.py loads the API key from `.env` to send training logs to W&B
python tools/train.py OUTPUT_DIR ./outputs/training

This script saves training results (trained weight files, model configurations, etc.) under ./outputs/training directory.

Evaluation

Run validation:

python tools/val.py --config ./outputs/training/config.yaml MODEL.WEIGHT ./outputs/training/model_best.pth OUTPUT_DIR  ./outputs/validation

This script saves validation results (validation score, prediction results, etc.) under ./outputs/validation directory.

Inference & Visualization

To be updated.