Journal ArticleDOI
An automatic pipeline for atlas-based fetal and neonatal brain segmentation and analysis
Andrea Urru,Ayako Nakaki,Oualid Benkarim,Francesca Crovetto,Laura Segalés,Valentin Comte,N.M. Hahner,Elisenda Eixarch,Eduard Gratacós,Fatima Crispi,Gemma Piella,Miguel Ángel González Ballester +11 more
- Vol. 230, pp
107334
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107334
TLDR
A new pipeline for fetal and neonatal segmentation has been developed and the introduction of the new templates together with the segmentation strategy leads to accurate results when compared to expert annotations, as well as better performances whenCompared to a reference pipeline (developing Human Connectome Project (dHCP).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.read more
Citations
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Journal ArticleDOI
Unsupervised Segmentation of Fetal Brain MRI using Deep Learning Cascaded Registration
Valentin Comte,Mireia Alenyá,Andrea Urru,Ayako Nakaki,Francesca Crovetto,Oscar Camara,Eduard Gratac'os,Elisenda Eixarch,Fatima Crispi,Gemma Piella,Mario Ceresa,Miguel Ángel González Ballester +11 more
TL;DR: In this paper , a cascaded deep learning network is proposed for 3D image registration, which computes small, incremental deformations to the moving image to align it precisely with the fixed image.
References
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