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Fetpype: An Open-Source Pipeline for Reproducible Fetal Brain MRI Analysis

Thomas Sanchez, Gerard Martí-Juan, David Meunier, Miguel Angel Gonzalez Ballester, Oscar Camara, Elisenda Eixarch, Gemma Piella, Meritxell Bach Cuadra, Guillaume Auzias

TL;DR

The paper addresses the reproducibility and integration challenges in fetal brain MRI analysis by introducing Fetpype, an open-source, Python-based framework that unifies preprocessing, super-resolution, segmentation, and surface extraction into an end-to-end pipeline. It leverages data standardization with BIDS, containerization, Nipype workflow management, and Hydra-configured YAML to ensure reproducible, scalable processing across local and HPC environments. By incorporating multiple established modules (e.g., NeSVoR, SVRTK, NiftyMIC, BOUNTI, Fetal-BET) within a cohesive, containerized workflow, Fetpype reduces bespoke scripting and cross-tool compatibility issues, promoting comparability across studies. The framework has undergone multi-site validation within European collaborations, supports ongoing community contributions, and plans for automated quality control and reporting, enhancing adoption in both research and clinical contexts.

Abstract

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.

Fetpype: An Open-Source Pipeline for Reproducible Fetal Brain MRI Analysis

TL;DR

The paper addresses the reproducibility and integration challenges in fetal brain MRI analysis by introducing Fetpype, an open-source, Python-based framework that unifies preprocessing, super-resolution, segmentation, and surface extraction into an end-to-end pipeline. It leverages data standardization with BIDS, containerization, Nipype workflow management, and Hydra-configured YAML to ensure reproducible, scalable processing across local and HPC environments. By incorporating multiple established modules (e.g., NeSVoR, SVRTK, NiftyMIC, BOUNTI, Fetal-BET) within a cohesive, containerized workflow, Fetpype reduces bespoke scripting and cross-tool compatibility issues, promoting comparability across studies. The framework has undergone multi-site validation within European collaborations, supports ongoing community contributions, and plans for automated quality control and reporting, enhancing adoption in both research and clinical contexts.

Abstract

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.

Paper Structure

This paper contains 5 sections, 1 figure.

Table of Contents

  1. Summary
  2. Statement of need
  3. State of the field
  4. Software design
  5. Research impact statement

Figures (1)

  • Figure 1: The different steps covered by Fetpype. Starting from several T2-weighted stacks of thick slices of the fetal brain (acquisition), Fetpype pre-processes data before feeding them to a super-resolution reconstruction algorithm that fuses them into a single high-resolution volume. This volume then undergoes segmentation, before moving to cortical surface extraction.