QC-Automator: Deep Learning-based Automated Quality Control for Diffusion MR Images

no code implementations • 15 Nov 2019 • Zahra Riahi Samani, Jacob Antony Alappatt, Drew Parker, Abdol Aziz Ould Ismail, Ragini Verma

QC-Automator uses convolutional neural networks along with transfer learning to train the automated artifact detection on a labeled dataset of ~332000 slices of dMRI data, from 155 unique subjects and 5 scanners with different dMRI acquisitions, achieving a 98% accuracy in detecting artifacts.

Artifact Detection Transfer Learning