Automated body organ segmentation, volumetry and population-averaged atlas for 3D motion-corrected T2-weighted fetal body MRI
Abstract Structural fetal body MRI provides true 3D information required for volumetry of fetal organs. However, current clinical and research practice primarily relies on manual slice-wise segmentation of raw T2-weighted stacks, which is time consuming, subject to inter- and intra-observer bias and...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-03-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-57087-x |
| _version_ | 1827310361927221248 |
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| author | Alena U. Uus Megan Hall Irina Grigorescu Carla Avena Zampieri Alexia Egloff Collado Kelly Payette Jacqueline Matthew Vanessa Kyriakopoulou Joseph V. Hajnal Jana Hutter Mary A. Rutherford Maria Deprez Lisa Story |
| author_facet | Alena U. Uus Megan Hall Irina Grigorescu Carla Avena Zampieri Alexia Egloff Collado Kelly Payette Jacqueline Matthew Vanessa Kyriakopoulou Joseph V. Hajnal Jana Hutter Mary A. Rutherford Maria Deprez Lisa Story |
| author_sort | Alena U. Uus |
| collection | DOAJ |
| description | Abstract Structural fetal body MRI provides true 3D information required for volumetry of fetal organs. However, current clinical and research practice primarily relies on manual slice-wise segmentation of raw T2-weighted stacks, which is time consuming, subject to inter- and intra-observer bias and affected by motion-corruption. Furthermore, there are no existing standard guidelines defining a universal approach to parcellation of fetal organs. This work produces the first parcellation protocol of the fetal body organs for motion-corrected 3D fetal body MRI. It includes 10 organ ROIs relevant to fetal quantitative volumetry studies. We also introduce the first population-averaged T2w MRI atlas of the fetal body. The protocol was used as a basis for training of a neural network for automated organ segmentation. It showed robust performance for different gestational ages. This solution minimises the need for manual editing and significantly reduces time. The general feasibility of the proposed pipeline was also assessed by analysis of organ growth charts created from automated parcellations of 91 normal control 3T MRI datasets that showed expected increase in volumetry during 22–38 weeks gestational age range. |
| first_indexed | 2024-04-24T19:58:33Z |
| format | Article |
| id | doaj.art-c8f02e69214b411da8f5ac2ea69e03aa |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-24T19:58:33Z |
| publishDate | 2024-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-c8f02e69214b411da8f5ac2ea69e03aa2024-03-24T12:15:15ZengNature PortfolioScientific Reports2045-23222024-03-0114111010.1038/s41598-024-57087-xAutomated body organ segmentation, volumetry and population-averaged atlas for 3D motion-corrected T2-weighted fetal body MRIAlena U. Uus0Megan Hall1Irina Grigorescu2Carla Avena Zampieri3Alexia Egloff Collado4Kelly Payette5Jacqueline Matthew6Vanessa Kyriakopoulou7Joseph V. Hajnal8Jana Hutter9Mary A. Rutherford10Maria Deprez11Lisa Story12School of Imaging Sciences and Biomedical Engineering, King’s College LondonCentre for the Developing Brain, King’s College LondonSchool of Imaging Sciences and Biomedical Engineering, King’s College LondonCentre for the Developing Brain, King’s College LondonCentre for the Developing Brain, King’s College LondonSchool of Imaging Sciences and Biomedical Engineering, King’s College LondonSchool of Imaging Sciences and Biomedical Engineering, King’s College LondonCentre for the Developing Brain, King’s College LondonSchool of Imaging Sciences and Biomedical Engineering, King’s College LondonSchool of Imaging Sciences and Biomedical Engineering, King’s College LondonCentre for the Developing Brain, King’s College LondonSchool of Imaging Sciences and Biomedical Engineering, King’s College LondonCentre for the Developing Brain, King’s College LondonAbstract Structural fetal body MRI provides true 3D information required for volumetry of fetal organs. However, current clinical and research practice primarily relies on manual slice-wise segmentation of raw T2-weighted stacks, which is time consuming, subject to inter- and intra-observer bias and affected by motion-corruption. Furthermore, there are no existing standard guidelines defining a universal approach to parcellation of fetal organs. This work produces the first parcellation protocol of the fetal body organs for motion-corrected 3D fetal body MRI. It includes 10 organ ROIs relevant to fetal quantitative volumetry studies. We also introduce the first population-averaged T2w MRI atlas of the fetal body. The protocol was used as a basis for training of a neural network for automated organ segmentation. It showed robust performance for different gestational ages. This solution minimises the need for manual editing and significantly reduces time. The general feasibility of the proposed pipeline was also assessed by analysis of organ growth charts created from automated parcellations of 91 normal control 3T MRI datasets that showed expected increase in volumetry during 22–38 weeks gestational age range.https://doi.org/10.1038/s41598-024-57087-x |
| spellingShingle | Alena U. Uus Megan Hall Irina Grigorescu Carla Avena Zampieri Alexia Egloff Collado Kelly Payette Jacqueline Matthew Vanessa Kyriakopoulou Joseph V. Hajnal Jana Hutter Mary A. Rutherford Maria Deprez Lisa Story Automated body organ segmentation, volumetry and population-averaged atlas for 3D motion-corrected T2-weighted fetal body MRI Scientific Reports |
| title | Automated body organ segmentation, volumetry and population-averaged atlas for 3D motion-corrected T2-weighted fetal body MRI |
| title_full | Automated body organ segmentation, volumetry and population-averaged atlas for 3D motion-corrected T2-weighted fetal body MRI |
| title_fullStr | Automated body organ segmentation, volumetry and population-averaged atlas for 3D motion-corrected T2-weighted fetal body MRI |
| title_full_unstemmed | Automated body organ segmentation, volumetry and population-averaged atlas for 3D motion-corrected T2-weighted fetal body MRI |
| title_short | Automated body organ segmentation, volumetry and population-averaged atlas for 3D motion-corrected T2-weighted fetal body MRI |
| title_sort | automated body organ segmentation volumetry and population averaged atlas for 3d motion corrected t2 weighted fetal body mri |
| url | https://doi.org/10.1038/s41598-024-57087-x |
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