Controlling variability in MRI analysis pipelines with software principles
Controlling Variability in~Neuroimaging Analysis Pipelines with~Software Product Lines
Software Engineering
Summary
MRI scans are complex and can produce noisy data, so scientists use processing steps called pipelines to make sense of them. Different teams often create their own pipelines, which can result in big differences even when studying the same brain data. The authors suggest using software product line (SPL) methods, which help manage different software versions, to organize and standardize these pipelines. This approach could make brain imaging studies more reliable and easier to compare.
What this means in practice
- •For neuroimaging data teams: Organize diverse MRI analysis pipelines using software product line methods to reduce inconsistency across projects.
- •For medical imaging software engineers: Design flexible MRI analysis tools by integrating variability control inspired by software product lines for consistent results.
A position paper. It proposes an approach and reports no results.
Authors
Gauthier Le Bartz Lyan, Paul Temple, Boris Clenet, Mathieu Acher
Abstract
Magnetic resonance imaging (MRI) is an acquisition technique used for medical diagnosis. It is notably used for structural and functional MRI (or fMRI) image analysis. MRI data is known to be complex and noisy, so pipelines are created to extract and enhance relevant information in retrieved images. The NARPS study highlighted the significant variability that can emerge from different pipelines set up by independent teams while trying to answer the same research question about brain functioning. Having different implementations realizing the same task is a typical playground for software product lines (SPLs), which may be out of the sight of neuroimage practitioners. Thus, proposing an SPL out of these different pipelines falls back on a typical reverse-engineering problem with its usual difficulties. In this document, we advocate for the conceptual adoption of SPL principles as a promising direction to improve reproducibility and consistency in neuroimaging research.