Projects

Deep Brainhack 2017 Projects

HCP Brain Mask Segmentation

Using the Human Connectome Project data, train an automatic system to compute brain masks on T1 data.

Healthy Brain Tissue Segmentation

Train automatic segmentation for healthy brain tissues in T1 images using the MICCAI 2012 dataset.

PET Brain Mask segmentation

Build and train a convolution neural net to automatically segment brain tissue for PET images.

Automatic Quality Control of ABIDE images

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

Oddball detection on fMRI and EEG data

https://openfmri.org/dataset/ds000116/

Imputation of missing modalities using voxel-based 3D CycleGAN

Train a generator/discriminator convolutional neural network to generate synthetic FLAIR/T1 from T1/FLAIR to enable use of complete datasets in instances of missing modalities.