An automatic pipeline for atlas-based fetal and neonatal brain segmentation and analysis. (March 2023)

• Record Type: Journal Article
• Authors: Urru, Andrea; Nakaki, Ayako; Benkarim, Oualid; Crovetto, Francesca; Segalés, Laura; Comte, Valentin; Hahner, Nadine; Eixarch, Elisenda; Gratacos, Eduard; Crispi, Fàtima; Piella, Gemma; González Ballester, Miguel A.
• Is Part Of: Computer methods and programs in biomedicine. Volume 230(2023)
• Publication Date: 2023-03
• Subjects: Segmentation -- Registration -- Atlas -- Brain -- MRI -- Fetal -- Neonatal -- Pipeline; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2023.107334
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A tool for perinatal brain segmentation is necessary for advanced studies in the field. We propose a unique, automatic framework for both fetal and neonatal brain MRI segmentation. We introduce a new multi-subject atlas for fetal MRI egional parcellation. We introduce an accurate, optimized approach for structural and multi-tissue segmentation, computed through a multi-channel, multi-resolution nonlinear registration to the developed templates. Abstract: Background and Objective: The automatic segmentation of perinatal brain structures in magnetic resonance imaging (MRI) is of utmost importance for the study of brain growth and related complications. While different methods exist for adult and pediatric MRI data, there is a lack for automatic tools for the analysis of perinatal imaging. Methods: In this work, a new pipeline for fetal and neonatal segmentation has been developed. We also report the creation of two new fetal atlases, and their use within the pipeline for atlas-based segmentation, based on novel registration methods. The pipeline is also able to extract cortical and pial surfaces and compute features, such as curvature, local gyrification index, sulcal depth, and thickness. Results: Results show that the introduction of the new templates together with our segmentation strategy leads to accurate results when compared to expert annotations, as well as better performances when compared to a reference pipeline (developing Human Connectome Project (dHCP)), for both early and late-onset fetal brains. Conclusions: These findings show the potential of the presented atlases and the whole pipeline for application in both fetal, neonatal, and longitudinal studies, which could lead to dramatic improvements in the understanding of perinatal brain development.