Spatio-Temporal Atlas of Normal Fetal Craniofacial Feature Development and CNN-Based Ocular Biometry for Motion-Corrected Fetal MRI

Alena Uus; Jacqueline Matthew; Irina Grigorescu; Samuel Jupp; Lucilio Cordero Grande; Anthony Price; Emer Hughes; Prachi Patkee; Vanessa Kyriakopoulou; Robert Wright; Thomas Roberts; Jana Hutter; Maximilian Pietsch; Joseph V. Hajnal; A. David Edwards; Mary Ann Rutherford; Maria Deprez

doi:10.1007/978-3-030-87735-4_16

Spatio-Temporal Atlas of Normal Fetal Craniofacial Feature Development and CNN-Based Ocular Biometry for Motion-Corrected Fetal MRI

Alena Uus^*, Jacqueline Matthew, Irina Grigorescu, Samuel Jupp, Lucilio Cordero Grande, Anthony Price, Emer Hughes, Prachi Patkee, Vanessa Kyriakopoulou, Robert Wright, Thomas Roberts, Jana Hutter, Maximilian Pietsch, Joseph V. Hajnal, A. David Edwards, Mary Ann Rutherford, Maria Deprez

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

4 Citations (Scopus)

Abstract

Motion-corrected fetal magnetic resonance imaging (MRI) is widely employed in large-scale fetal brain studies. However, the current processing pipelines and spatio-temporal atlases tend to omit craniofacial structures, which are known to be linked to genetic syndromes. In this work, we present the first spatio-temporal atlas of the fetal head that includes craniofacial features and covers 21 to 36 weeks gestational age range. Additionally, we propose a fully automated pipeline for fetal ocular biometry based on a 3D convolutional neural network (CNN). The extracted biometric indices are used for the growth trajectory analysis of changes in ocular metrics for 253 normal fetal subjects from the developing human connectome project (dHCP).

Original language	English
Title of host publication	Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Placental and Preterm Image Analysis - 3rd International Workshop, UNSURE 2021, and 6th International Workshop, PIPPI 2021, Held in Conjunction with MICCAI 2021, Proceedings
Editors	Carole H. Sudre, Roxane Licandro, Christian Baumgartner, Andrew Melbourne, Adrian Dalca, Jana Hutter, Ryutaro Tanno, Esra Abaci Turk, Koen Van Leemput, Jordina Torrents Barrena, William M. Wells, Christopher Macgowan
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	168-178
Number of pages	11
ISBN (Print)	9783030877347
DOIs	https://doi.org/10.1007/978-3-030-87735-4_16
Publication status	Published - 2021
Event	3rd International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2021, and the 6th International Workshop on Perinatal, Preterm and Paediatric Image Analysis, PIPPI 2021, held in conjunction with the 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021 - Virtual, Online Duration: 1 Oct 2021 → 1 Oct 2021

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12959 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	3rd International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2021, and the 6th International Workshop on Perinatal, Preterm and Paediatric Image Analysis, PIPPI 2021, held in conjunction with the 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021
City	Virtual, Online
Period	1/10/2021 → 1/10/2021

Keywords

Automated biometry
Craniofacial features
Motion-corrected fetal MRI
Ocular measurements
Spatio-temporal atlas

Access to Document

10.1007/978-3-030-87735-4_16

Cite this

Uus, A., Matthew, J., Grigorescu, I., Jupp, S., Grande, L. C., Price, A., Hughes, E., Patkee, P., Kyriakopoulou, V., Wright, R., Roberts, T., Hutter, J., Pietsch, M., Hajnal, J. V., Edwards, A. D., Rutherford, M. A., & Deprez, M. (2021). Spatio-Temporal Atlas of Normal Fetal Craniofacial Feature Development and CNN-Based Ocular Biometry for Motion-Corrected Fetal MRI. In C. H. Sudre, R. Licandro, C. Baumgartner, A. Melbourne, A. Dalca, J. Hutter, R. Tanno, E. Abaci Turk, K. Van Leemput, J. Torrents Barrena, W. M. Wells, & C. Macgowan (Eds.), Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Placental and Preterm Image Analysis - 3rd International Workshop, UNSURE 2021, and 6th International Workshop, PIPPI 2021, Held in Conjunction with MICCAI 2021, Proceedings (pp. 168-178). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12959 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-87735-4_16

Uus, Alena ; Matthew, Jacqueline ; Grigorescu, Irina et al. / Spatio-Temporal Atlas of Normal Fetal Craniofacial Feature Development and CNN-Based Ocular Biometry for Motion-Corrected Fetal MRI. Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Placental and Preterm Image Analysis - 3rd International Workshop, UNSURE 2021, and 6th International Workshop, PIPPI 2021, Held in Conjunction with MICCAI 2021, Proceedings. editor / Carole H. Sudre ; Roxane Licandro ; Christian Baumgartner ; Andrew Melbourne ; Adrian Dalca ; Jana Hutter ; Ryutaro Tanno ; Esra Abaci Turk ; Koen Van Leemput ; Jordina Torrents Barrena ; William M. Wells ; Christopher Macgowan. Springer Science and Business Media Deutschland GmbH, 2021. pp. 168-178 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Motion-corrected fetal magnetic resonance imaging (MRI) is widely employed in large-scale fetal brain studies. However, the current processing pipelines and spatio-temporal atlases tend to omit craniofacial structures, which are known to be linked to genetic syndromes. In this work, we present the first spatio-temporal atlas of the fetal head that includes craniofacial features and covers 21 to 36 weeks gestational age range. Additionally, we propose a fully automated pipeline for fetal ocular biometry based on a 3D convolutional neural network (CNN). The extracted biometric indices are used for the growth trajectory analysis of changes in ocular metrics for 253 normal fetal subjects from the developing human connectome project (dHCP).",

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author = "Alena Uus and Jacqueline Matthew and Irina Grigorescu and Samuel Jupp and Grande, {Lucilio Cordero} and Anthony Price and Emer Hughes and Prachi Patkee and Vanessa Kyriakopoulou and Robert Wright and Thomas Roberts and Jana Hutter and Maximilian Pietsch and Hajnal, {Joseph V.} and Edwards, {A. David} and Rutherford, {Mary Ann} and Maria Deprez",

note = "Funding Information: We thank everyone who was involved in acquisition and analysis of the datasets at the Department of Perinatal Imaging and Health at King?s College London. We thank all participating mothers. This work was supported by the European Research Council under the European Union?s Seventh Framework Programme [FP7/ 20072013]/ERC grant agreement no. 319456 dHCP project, the Wellcome/EPSRC Centre for Medical Engineering at King?s College London [WT 203148/Z/16/Z)], the NIHR Clinical Research Facility (CRF) at Guy?s and St Thomas? and by the National Institute for Health Research Biomedical Research Centre based at Guy?s and St Thomas? NHS Foundation Trust and King?s College London. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health. Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 3rd International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2021, and the 6th International Workshop on Perinatal, Preterm and Paediatric Image Analysis, PIPPI 2021, held in conjunction with the 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021 ; Conference date: 01-10-2021 Through 01-10-2021",

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booktitle = "Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Placental and Preterm Image Analysis - 3rd International Workshop, UNSURE 2021, and 6th International Workshop, PIPPI 2021, Held in Conjunction with MICCAI 2021, Proceedings",

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Uus, A, Matthew, J, Grigorescu, I, Jupp, S, Grande, LC, Price, A, Hughes, E, Patkee, P , Kyriakopoulou, V , Wright, R , Roberts, T , Hutter, J , Pietsch, M , Hajnal, JV , Edwards, AD , Rutherford, MA & Deprez, M 2021, Spatio-Temporal Atlas of Normal Fetal Craniofacial Feature Development and CNN-Based Ocular Biometry for Motion-Corrected Fetal MRI. in CH Sudre, R Licandro, C Baumgartner, A Melbourne, A Dalca, J Hutter, R Tanno, E Abaci Turk, K Van Leemput, J Torrents Barrena, WM Wells & C Macgowan (eds), Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Placental and Preterm Image Analysis - 3rd International Workshop, UNSURE 2021, and 6th International Workshop, PIPPI 2021, Held in Conjunction with MICCAI 2021, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12959 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 168-178, 3rd International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2021, and the 6th International Workshop on Perinatal, Preterm and Paediatric Image Analysis, PIPPI 2021, held in conjunction with the 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021, Virtual, Online, 1/10/2021. https://doi.org/10.1007/978-3-030-87735-4_16

Spatio-Temporal Atlas of Normal Fetal Craniofacial Feature Development and CNN-Based Ocular Biometry for Motion-Corrected Fetal MRI. / Uus, Alena; Matthew, Jacqueline; Grigorescu, Irina et al.
Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Placental and Preterm Image Analysis - 3rd International Workshop, UNSURE 2021, and 6th International Workshop, PIPPI 2021, Held in Conjunction with MICCAI 2021, Proceedings. ed. / Carole H. Sudre; Roxane Licandro; Christian Baumgartner; Andrew Melbourne; Adrian Dalca; Jana Hutter; Ryutaro Tanno; Esra Abaci Turk; Koen Van Leemput; Jordina Torrents Barrena; William M. Wells; Christopher Macgowan. Springer Science and Business Media Deutschland GmbH, 2021. p. 168-178 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12959 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

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AU - Jupp, Samuel

AU - Grande, Lucilio Cordero

AU - Price, Anthony

AU - Hughes, Emer

AU - Patkee, Prachi

AU - Kyriakopoulou, Vanessa

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AU - Roberts, Thomas

AU - Hutter, Jana

AU - Pietsch, Maximilian

AU - Hajnal, Joseph V.

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Uus A, Matthew J, Grigorescu I, Jupp S, Grande LC, Price A et al. Spatio-Temporal Atlas of Normal Fetal Craniofacial Feature Development and CNN-Based Ocular Biometry for Motion-Corrected Fetal MRI. In Sudre CH, Licandro R, Baumgartner C, Melbourne A, Dalca A, Hutter J, Tanno R, Abaci Turk E, Van Leemput K, Torrents Barrena J, Wells WM, Macgowan C, editors, Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Placental and Preterm Image Analysis - 3rd International Workshop, UNSURE 2021, and 6th International Workshop, PIPPI 2021, Held in Conjunction with MICCAI 2021, Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. p. 168-178. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-87735-4_16