Newest papers

Controlling Variability in~Neuroimaging Analysis Pipelines with~Software Product Lines

Gauthier Le Bartz Lyan, Paul Temple, Boris Clénet, Mathieu Acher · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.29821 · cited by 0 works (OpenAlex) · open-access PDF

Spanish

La resonancia magnética (MRI) es una técnica de adquisición utilizada para el diagnóstico médico. Se utiliza notablemente para el análisis de imágenes de resonancia magnética estructural y funcional (o fMRI). Se sabe que los datos de resonancia magnética son complejos y ruidosos, por lo que se crean tuberías para extraer y mejorar la información relevante en las imágenes recuperadas. El estudio NARPS destacó la variabilidad significativa que puede surgir de diferentes tuberías establecidas por equipos independientes mientras intenta responder a la misma pregunta de investigación sobre el funcionamiento del cerebro. Tener diferentes implementaciones realizando la misma tarea es un terreno de juego típico para líneas de productos de software (SPLs), que puede estar fuera de la vista de los profesionales de la neuroimagen. Por lo tanto, proponer una SPL

Machine translation (Meta M2M100); the English original follows.

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.

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Topics: Pipeline transport, Consistency (knowledge bases), Computer science, Product (mathematics), Software, Task (project management)

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