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

Gauthier Le Bartz Lyan, Paul Temple, Boris Clenet, Mathieu Acher · 2026 · arXiv

DOI: 10.48550/arXiv.2610.29821 · open-access PDF

Korean

Magnetic Resonance Imaging (MRI)는 의료 진단에 사용되는 획득 기술입니다. 그것은 특히 구조적 및 기능적 MRI (또는 fMRI) 이미지 분석에 사용됩니다. MRI 데이터는 복잡하고 시끄러운 것으로 알려져 있으므로 파이프라인은 추출 된 이미지에서 관련 정보를 추출하고 향상시키기 위해 만들어집니다. NARPS 연구는 독립적 인 팀이 두뇌 기능에 관한 동일한 연구 질문에 답하려고하는 동안 다른 파이프라인에서 나타날 수있는 중요한 변화를 강조했습니다. 동일한 작업을 실현하는 다른 구현이 소프트웨어 프로듀서의 전형적인 놀이터입니다.

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 produ

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