DEEP QC

Code for the paper Vladimir S. Fonov, Mahsa Dadar, The PREVENT-AD Research Group, D. Louis Collins "DARQ: Deep learning of quality control for stereotaxic registration of human brain MRI".

Updated version of the previosly available "Deep learning of quality control for stereotaxic registration of human brain MRI"

Installation (Python version) using conda for inference

• CPU version

  conda install pytorch-cpu==1.7.1 torchvision==0.8.2 cpuonly -c pytorch 
  conda install scikit-image
  

• GPU version

  conda install pytorch==1.7.1 torchvision==0.8.2 cudatoolkit=<your cuda version>  -c pytorch 
  conda install scikit-image
  

• (optional) minc toolkit and minc2-simple

  conda install -c vfonov minc-toolkit-v2 minc2-simple
  

• (optional) nibabel (for analyzing Freesurfer output)

  pip install nibabel
  

Running

Inference

• Inference using jpeg files generated by minc_aqc.pl script: python3 python/aqc_apply.py --net <network> --image <image base>
• Inference using minc files in stereotaxic space: python3 python/aqc_apply.py --net <network> --volume <input.mnc>
• Inference using Freesurfer output: python3 python/aqc_apply.py --net <network> --freesurfer <freesurfer subject directory>

Training

• Training in python directory run_all_experiments.sh - will try to train all networks

Dependencies

• trainig dependencies: scikit-image tensorboard,
• for inference directly on minc files minc2-simple
• for inference on freeserfer files: nibabel
• minc2-simple (optional): https://github.com/vfonov/minc2-simple

Files

• Shell scripts:
  • download_minimal_models.sh - download QCResNET-18 with reference pretrained model to run automatic qc (43mb)
  • download_all_models.sh - download all pretrained models to run automatic qc
• Directory python:
  • run_all_experiments.sh - run experiments with different versions of ResNet and SquezeNet
  • aqc_apply.py - apply pre-trained network
  • aqc_convert_to_cpu.py- helper script to convert network from GPU to CPU
  • aqc_data.py - module to load QC data
  • aqc_training.py - deep nearal net training script
  • model/resnet_qc.py - module with ResNET implementation, based on https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
  • model/util.py - various helper functions
  • *.R - R scripts to generete figures for the paper
• Image files:
  • mni_icbm152_t1_tal_nlin_sym_09c_0.jpg,mni_icbm152_t1_tal_nlin_sym_09c_1.jpg,mni_icbm152_t1_tal_nlin_sym_09c_2.jpg - reference slices, needed for both training and running pretrained model
• results - figures for the paper
• data - reference images

Validating correct operation (requires minc-toolkit and minc2_simple python module)

# create a file with 30 degree rotation transform
param2xfm -rotations 30 0 0  rot_30.xfm
# apply to a template:
itk_resample /opt/minc/share/icbm152_model_09c/mni_icbm152_t1_tal_nlin_sym_09c.mnc --transform rot_30.xfm bad.mnc

# run QC script on good scan
# should print "Pass"
python python/aqc_apply.py --net r18 --volume /opt/minc/share/icbm152_model_09c/mni_icbm152_t1_tal_nlin_sym_09c.mnc

# now on "bad"
# should print "Fail"
python python/aqc_apply.py --net r18 --volume bad.mnc

Validating correct operation on Freesurfer, requires nibabel

# run recon-all on ernie subject from freesurfer installation
recon-all -s ernie -i $SUBJECTS_DIR/sample-001.mgz -i $SUBJECTS_DIR/sample-002.mgz -autorecon1

# run automated QC on freesurfer output
# should pring "Pass"
python python/aqc_apply.py --net r18 --freesurfer $FREESURFER_HOME/subjects/ernie