Fetpype: An Open-Source Pipeline for Reproducible Fetal Brain MRI Analysis
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arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866910009453969408 |
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| author | Sanchez, Thomas Martí-Juan, Gerard Meunier, David Ballester, Miguel Angel Gonzalez Camara, Oscar Eixarch, Elisenda Piella, Gemma Cuadra, Meritxell Bach Auzias, Guillaume |
| author_facet | Sanchez, Thomas Martí-Juan, Gerard Meunier, David Ballester, Miguel Angel Gonzalez Camara, Oscar Eixarch, Elisenda Piella, Gemma Cuadra, Meritxell Bach Auzias, Guillaume |
| contents | Fetal brain magnetic resonance imaging (MRI) is crucial for assessing neurodevelopment in utero. However, fetal MRI analysis remains technically challenging due to fetal motion, low signal-to-noise ratio, and the need for complex multi-step processing pipelines. These pipelines typically include motion correction, super-resolution reconstruction, tissue segmentation, and cortical surface extraction. While specialized tools exist for each individual processing step, integrating them into a robust, reproducible, and user-friendly end-to-end workflow remains difficult. This fragmentation limits reproducibility across studies and hinders the adoption of advanced fetal neuroimaging methods in both research and clinical contexts. Fetpype addresses this gap by providing a standardized, modular, and reproducible framework for fetal brain MRI preprocessing and analysis, enabling researchers to process raw T2-weighted acquisitions through to derived volumetric and surface-based outputs within a unified workflow. Fetpype is publicly available on GitHub at https://github.com/fetpype/fetpype. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_17472 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fetpype: An Open-Source Pipeline for Reproducible Fetal Brain MRI Analysis Sanchez, Thomas Martí-Juan, Gerard Meunier, David Ballester, Miguel Angel Gonzalez Camara, Oscar Eixarch, Elisenda Piella, Gemma Cuadra, Meritxell Bach Auzias, Guillaume Image and Video Processing Fetal brain magnetic resonance imaging (MRI) is crucial for assessing neurodevelopment in utero. However, fetal MRI analysis remains technically challenging due to fetal motion, low signal-to-noise ratio, and the need for complex multi-step processing pipelines. These pipelines typically include motion correction, super-resolution reconstruction, tissue segmentation, and cortical surface extraction. While specialized tools exist for each individual processing step, integrating them into a robust, reproducible, and user-friendly end-to-end workflow remains difficult. This fragmentation limits reproducibility across studies and hinders the adoption of advanced fetal neuroimaging methods in both research and clinical contexts. Fetpype addresses this gap by providing a standardized, modular, and reproducible framework for fetal brain MRI preprocessing and analysis, enabling researchers to process raw T2-weighted acquisitions through to derived volumetric and surface-based outputs within a unified workflow. Fetpype is publicly available on GitHub at https://github.com/fetpype/fetpype. |
| title | Fetpype: An Open-Source Pipeline for Reproducible Fetal Brain MRI Analysis |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2512.17472 |