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Projects
Build and train a convolution neural net to automatically segment brain tissue for PET images.
Using the Human Connectome Project data, train an automatic system to compute brain masks on T1 data.
Train automatic segmentation for healthy brain tissues in T1 images. Data generously provided by Neuromorphometrics. This data was used for the MICCAI 2012 Grand Challenge on Multi-Atlas Labeling
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
https://openfmri.org/dataset/ds000116/
Using voxel-based 3D CycleGAN, train a generator/discriminator convolutional neural network to generate synthetic FLAIR/T1 from T1/FLAIR to enable the use of complete datasets in instances of missing modalities.
Train a neural network to reconstruct dynamically growing axonal arbours collected using 3D 2-photon microscopy of individually labeled neurons in living animals with a database of manual reconstructions. The primary objective will be to track changes in individual branches over time.
