UC3 UNITOBrain

This repository contains the source code for getting started on UniToBrain dataset by using pyEDDL/pyECVL

Requirements

• numpy
• pyeddl
• pyecvl
• cv2
• wandb
• scipy
• pydicom

1) Preprocessing Run Command

The network inputs are tensors of multiple CT images at the same scansion height for each patient. This script creates a folder called input_tensored into the prep_output_path path.

Image size of 128 pixels is used for the pratrain phase, size of 512 for the full resolution training

# 1.1) compute the inputs
python3 -u dicomtopt.py --rescale_size 128 --prep_output_path <output-path> --unitobrain_path <unitobrain-path> --target 'INPUT'

# 1.2) compute the target perfusion maps
python3 -u dicomtopt.py --rescale_size 128 --prep_output_path <output-path> --unitobrain_path <unitobrain-path> --target 'TTP'
python3 -u dicomtopt.py --rescale_size 128 --prep_output_path <output-path> --unitobrain_path <unitobrain-path> --target 'CBF'
python3 -u dicomtopt.py --rescale_size 128 --prep_output_path <output-path> --unitobrain_path <unitobrain-path> --target 'CBV'

2) Pretrain Run Command (Optional)

Pretrained model on lower resolution tensors (target TTP on 4 gpus)

python3 -u train_model.py --target 'TTP' --shape 128 --lr 1e-5 --num_gpu 4 --epochs 100 --batch-size 8 --mem 'low_mem' --name <run-name> <prep_output_path>

3) Train Run Command

Train the model on full resolution tensors (target TTP on 4 gpus)

python3 -u train_model.py --target 'TTP' --resume_ckpts <pretrain-checkpoint> --batch-size 8 --batch-size-val 4 --lr 1e-5 --epochs 50 --num_gpu 4 --name <run-name> --mem 'low_mem' --shape 512 --log-interval 20 <prep_output_path>

4) Inference Run Command

Inference command to run Tests only

python3 -u test_model.py --target 'TTP' --shape 512 --gpu 4 --mem 'low_mem' --ckpts <train-checkpoint> <prep_output_path>