The Stanford Centre for Reproducible Neuroscience has been working on quality control of MRI images using an automatic pipeline that computes 64 image quality metrics and uses them to train an automatic classifier, but have not been able to generalize it to new sites with different MRI parameters. Read their pre-print here: http://www.biorxiv.org/content/early/2017/07/15/111294
The code for the mriqc is here: https://github.com/poldracklab/mriqc
They would like us to try to learn their QC labels and see if deep learning can generalize better than their random forest/SVM's trained on imaging metrics
Here's a writeup by Carolina Makowski about one possible quality control protocol for manual labeling: https://www.dropbox.com/s/4it50fjez1sibta/Structural_MR_QC_forLepageLab_10-08-2016.pdf?dl=0
source: https://github.com/brainhack101/deepbrainhack2017/wiki
The Stanford Centre for Reproducible Neuroscience has been working on quality control of MRI images using an automatic pipeline that computes 64 image quality metrics and uses them to train an automatic classifier, but have not been able to generalize it to new sites with different MRI parameters. Read their pre-print here: http://www.biorxiv.org/content/early/2017/07/15/111294
The code for the mriqc is here: https://github.com/poldracklab/mriqc
They would like us to try to learn their QC labels and see if deep learning can generalize better than their random forest/SVM's trained on imaging metrics
Here's a writeup by Carolina Makowski about one possible quality control protocol for manual labeling: https://www.dropbox.com/s/4it50fjez1sibta/Structural_MR_QC_forLepageLab_10-08-2016.pdf?dl=0
source: https://github.com/brainhack101/deepbrainhack2017/wiki