VisualQC: software development kit for medical and neuroimaging quality control and assurance

Abstract

VisualQC is a medical imaging software library aimed to enable and improve certain challenging aspects of neuroimaging quality control (niQC). VisualQC is purpose-built for rigorous niQC and aims to greatly reduce the tediousness of manual visual QC. It achieves this by seamlessly (1) presenting relevant composite visualizations while alerting the user of any outliers based on advanced machine learning algorithms, (2) offering an easy way to record the ratings and notes, and (3) making it easy to quickly navigate through a large number of subjects. VisualQC offers a modular and extensible framework, to allow for solving a wide diversity of visual niQC tasks along with some assistive automation. We demonstrate this by showing a few common but diverse QC use-cases targeting visual review and rating of (1) the raw image quality for structural and functional MRI scans, (2) accuracy of anatomical segmentations either via Freesurfer or a generic voxel-based segmentation algorithm, (3) accuracy of the alignment between two images (registration algorithms), and (4) accuracy of defacing algorithms to protect patient privacy. We believe this modular and extensible API/classes will encourage the community to customize it for their own needs and with their own visionary ideas and encourage them to share their implementation with the community to improve the quality of neuroimaging data and analyses.