An automatic pipeline for atlas-based fetal and neonatal brain segmentation and analysis
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arXiv
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2022
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| _version_ | 1866910704553951232 |
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| author | Urru, Andrea Nakaki, Ayako Benkarim, Oualid Crovetto, Francesca Segales, Laura Comte, Valentin Hahner, Nadine Eixarch, Elisenda Gratacós, Eduard Crispi, Fàtima Piella, Gemma Ballester, Miguel A González |
| author_facet | Urru, Andrea Nakaki, Ayako Benkarim, Oualid Crovetto, Francesca Segales, Laura Comte, Valentin Hahner, Nadine Eixarch, Elisenda Gratacós, Eduard Crispi, Fàtima Piella, Gemma Ballester, Miguel A González |
| contents | 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. 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, thickness, sulcal depth, and local gyrification index. 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2205_07575 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | An automatic pipeline for atlas-based fetal and neonatal brain segmentation and analysis Urru, Andrea Nakaki, Ayako Benkarim, Oualid Crovetto, Francesca Segales, Laura Comte, Valentin Hahner, Nadine Eixarch, Elisenda Gratacós, Eduard Crispi, Fàtima Piella, Gemma Ballester, Miguel A González Computer Vision and Pattern Recognition Quantitative Methods 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. 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, thickness, sulcal depth, and local gyrification index. 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. |
| title | An automatic pipeline for atlas-based fetal and neonatal brain segmentation and analysis |
| topic | Computer Vision and Pattern Recognition Quantitative Methods |
| url | https://arxiv.org/abs/2205.07575 |