Regional volumetric assessment of perinatal brain development is currently limited by the lack of consistent high quality multi-regional segmentation methods applicable to both fetal and neonatal MRI. We present Multi-BOUNTI, a deep learning pipeline for automated multi-lobe segmentation of fetal and neonatal T2w brain MRI. The method is based on a dedicated 43-label parcellation protocol and a 3D Attention U-Net trained on brain MRI datasets of subjects spanning 21–44 weeks gestational/postmenstrual age. The pipeline integrates preprocessing, segmentation and volumetric analysis, and was evaluated on independent datasets, demonstrating fast (< 10 min/case) and accurate performance with high agreement to manually refined labels.

We demonstrate the application of the framework with 267 fetal and 593 neonatal MRI datasets from the developing Human Connectome Project without reported clinically significant brain anomalies to derive normative volumetric growth models across 21–44 weeks GA/PMA. These models were used to characterise developmental trajectories, assess differences between fetal and preterm neonatal cohorts, and analyse longitudinal changes. The resulting normative models were integrated into an automated reporting framework enabling subject-specific volumetric assessment via centiles and z-scores.

Multi-BOUNTI provides a unified and scalable approach for perinatal brain segmentation and volumetry, supporting large-scale studies and facilitating future clinical translation. The full pipeline is publicly available at https://github.com/SVRTK/perinatal-brain-mri-analysis.

The authors have declared no competing interest.

This work was supported by Rosetrees Trust awarded to MR [2817415], NIHR Advanced Fellowship awarded to LS [NIHR30166], AMR GN4028 awarded to MR [3788508], the European Research Council under the European Unions Seventh Framework Programme [FP7/ 20072013]/ERC grant agreement no. 319456 dHCP project, the BIBS project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 777394, the Wellcome/ EPSRC Centre for Medical Engineering at Kings College London [WT 203148/Z/16/Z], MRC Centre for Neurodevelopmental Disorders Kings College London [MR/N026063/1], the NIHR Clinical Research Facility (CRF) at Guys and St Thomas and by the National Institute for Health Research Biomedical Research Centre based at Guys and St Thomas NHS Foundation Trust and Kings College London. TA is funded by an MRC Senior Clinical Fellowship [MR/Y009665/1].

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The study used only the publicly available perinatal MRI datasets from the dHCP project: https://www.developingconnectome.org/data-release

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