Automated craniofacial biometry with 3D T2w fetal MRI

Jacqueline Matthew; Alena Uus; Alexia Egloff Collado; Aysha Luis; Sophie Arulkumaran; Abi Fukami-Gartner; Vanessa Kyriakopoulou; Daniel Cromb; Robert Wright; Kathleen Colford; Maria Deprez; Jana Hutter; Jonathan O’Muircheartaigh; Christina Malamateniou; Reza Razavi; Lisa Story; Joseph V. Hajnal; Mary A. Rutherford

doi:10.1371/journal.pdig.0000663

Automated craniofacial biometry with 3D T2w fetal MRI

Jacqueline Matthew^*, Alena Uus, Alexia Egloff Collado, Aysha Luis, Sophie Arulkumaran, Abi Fukami-Gartner, Vanessa Kyriakopoulou, Daniel Cromb, Robert Wright, Kathleen Colford, Maria Deprez, Jana Hutter, Jonathan O’Muircheartaigh, Christina Malamateniou, Reza Razavi, Lisa Story, Joseph V. Hajnal, Mary A. Rutherford

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

Abstract

Objectives Evaluating craniofacial phenotype-genotype correlations prenatally is increasingly important; however, it is subjective and challenging with 3D ultrasound. We developed an automated label propagation pipeline using 3D motion- corrected, slice-to-volume reconstructed (SVR) fetal MRI for craniofacial measurements. Methods A literature review and expert consensus identified 31 craniofacial biometrics for fetal MRI. An MRI atlas with defined anatomical landmarks served as a template for subject registration, auto-labelling, and biometric calculation. We assessed 108 healthy controls and 24 fetuses with Down syndrome (T21) in the third trimester (29–36 weeks gestational age, GA) to identify meaningful biometrics in T21. Reliability and reproducibility were evaluated in 10 random datasets by four observers. Results Automated labels were produced for all 132 subjects with a 0.3% placement error rate. Seven measurements, including anterior base of skull length and maxillary length, showed significant differences with large effect sizes between T21 and control groups (ANOVA, p<0.001). Manual measurements took 25–35 minutes per case, while automated extraction took approximately 5 minutes. Bland-Altman plots showed agreement within manual observer ranges except for mandibular width, which had higher variability. Extended GA growth charts (19–39 weeks), based on 280 control fetuses, were produced for future research. Conclusion This is the first automated atlas-based protocol using 3D SVR MRI for fetal craniofacial biometrics, accurately revealing morphological craniofacial differences in a T21 cohort. Future work should focus on improving measurement reliability, larger clinical cohorts, and technical advancements, to enhance prenatal care and phenotypic characterisation.

Original language	English
Article number	e0000663
Journal	PLOS digital health
Volume	3
Issue number	12
DOIs	https://doi.org/10.1371/journal.pdig.0000663
Publication status	Published - Dec 2024

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1371/journal.pdig.0000663

Cite this

Matthew, J., Uus, A., Collado, A. E., Luis, A., Arulkumaran, S., Fukami-Gartner, A., Kyriakopoulou, V., Cromb, D., Wright, R., Colford, K., Deprez, M., Hutter, J., O’Muircheartaigh, J., Malamateniou, C., Razavi, R., Story, L., Hajnal, J. V., & Rutherford, M. A. (2024). Automated craniofacial biometry with 3D T2w fetal MRI. PLOS digital health, 3(12), Article e0000663. https://doi.org/10.1371/journal.pdig.0000663

@article{3e1212c280eb422cae310b48ce2d9265,

title = "Automated craniofacial biometry with 3D T2w fetal MRI",

abstract = "Objectives Evaluating craniofacial phenotype-genotype correlations prenatally is increasingly important; however, it is subjective and challenging with 3D ultrasound. We developed an automated label propagation pipeline using 3D motion- corrected, slice-to-volume reconstructed (SVR) fetal MRI for craniofacial measurements. Methods A literature review and expert consensus identified 31 craniofacial biometrics for fetal MRI. An MRI atlas with defined anatomical landmarks served as a template for subject registration, auto-labelling, and biometric calculation. We assessed 108 healthy controls and 24 fetuses with Down syndrome (T21) in the third trimester (29–36 weeks gestational age, GA) to identify meaningful biometrics in T21. Reliability and reproducibility were evaluated in 10 random datasets by four observers. Results Automated labels were produced for all 132 subjects with a 0.3% placement error rate. Seven measurements, including anterior base of skull length and maxillary length, showed significant differences with large effect sizes between T21 and control groups (ANOVA, p<0.001). Manual measurements took 25–35 minutes per case, while automated extraction took approximately 5 minutes. Bland-Altman plots showed agreement within manual observer ranges except for mandibular width, which had higher variability. Extended GA growth charts (19–39 weeks), based on 280 control fetuses, were produced for future research. Conclusion This is the first automated atlas-based protocol using 3D SVR MRI for fetal craniofacial biometrics, accurately revealing morphological craniofacial differences in a T21 cohort. Future work should focus on improving measurement reliability, larger clinical cohorts, and technical advancements, to enhance prenatal care and phenotypic characterisation.",

author = "Jacqueline Matthew and Alena Uus and Collado, {Alexia Egloff} and Aysha Luis and Sophie Arulkumaran and Abi Fukami-Gartner and Vanessa Kyriakopoulou and Daniel Cromb and Robert Wright and Kathleen Colford and Maria Deprez and Jana Hutter and Jonathan O{\textquoteright}Muircheartaigh and Christina Malamateniou and Reza Razavi and Lisa Story and Hajnal, {Joseph V.} and Rutherford, {Mary A.}",

note = "Publisher Copyright: {\textcopyright} 2024 Matthew et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.",

year = "2024",

month = dec,

doi = "10.1371/journal.pdig.0000663",

language = "English",

volume = "3",

journal = "PLOS digital health",

issn = "2767-3170",

publisher = "Public Library of Science",

number = "12",

}

Matthew, J, Uus, A, Collado, AE, Luis, A , Arulkumaran, S, Fukami-Gartner, A, Kyriakopoulou, V, Cromb, D, Wright, R, Colford, K, Deprez, M, Hutter, J, O’Muircheartaigh, J, Malamateniou, C , Razavi, R , Story, L , Hajnal, JV & Rutherford, MA 2024, 'Automated craniofacial biometry with 3D T2w fetal MRI', PLOS digital health, vol. 3, no. 12, e0000663. https://doi.org/10.1371/journal.pdig.0000663

TY - JOUR

T1 - Automated craniofacial biometry with 3D T2w fetal MRI

AU - Matthew, Jacqueline

AU - Uus, Alena

AU - Collado, Alexia Egloff

AU - Luis, Aysha

AU - Arulkumaran, Sophie

AU - Fukami-Gartner, Abi

AU - Kyriakopoulou, Vanessa

AU - Cromb, Daniel

AU - Wright, Robert

AU - Colford, Kathleen

AU - Deprez, Maria

AU - Hutter, Jana

AU - O’Muircheartaigh, Jonathan

AU - Malamateniou, Christina

AU - Razavi, Reza

AU - Story, Lisa

AU - Hajnal, Joseph V.

AU - Rutherford, Mary A.

N1 - Publisher Copyright: © 2024 Matthew et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

PY - 2024/12

Y1 - 2024/12

N2 - Objectives Evaluating craniofacial phenotype-genotype correlations prenatally is increasingly important; however, it is subjective and challenging with 3D ultrasound. We developed an automated label propagation pipeline using 3D motion- corrected, slice-to-volume reconstructed (SVR) fetal MRI for craniofacial measurements. Methods A literature review and expert consensus identified 31 craniofacial biometrics for fetal MRI. An MRI atlas with defined anatomical landmarks served as a template for subject registration, auto-labelling, and biometric calculation. We assessed 108 healthy controls and 24 fetuses with Down syndrome (T21) in the third trimester (29–36 weeks gestational age, GA) to identify meaningful biometrics in T21. Reliability and reproducibility were evaluated in 10 random datasets by four observers. Results Automated labels were produced for all 132 subjects with a 0.3% placement error rate. Seven measurements, including anterior base of skull length and maxillary length, showed significant differences with large effect sizes between T21 and control groups (ANOVA, p<0.001). Manual measurements took 25–35 minutes per case, while automated extraction took approximately 5 minutes. Bland-Altman plots showed agreement within manual observer ranges except for mandibular width, which had higher variability. Extended GA growth charts (19–39 weeks), based on 280 control fetuses, were produced for future research. Conclusion This is the first automated atlas-based protocol using 3D SVR MRI for fetal craniofacial biometrics, accurately revealing morphological craniofacial differences in a T21 cohort. Future work should focus on improving measurement reliability, larger clinical cohorts, and technical advancements, to enhance prenatal care and phenotypic characterisation.

AB - Objectives Evaluating craniofacial phenotype-genotype correlations prenatally is increasingly important; however, it is subjective and challenging with 3D ultrasound. We developed an automated label propagation pipeline using 3D motion- corrected, slice-to-volume reconstructed (SVR) fetal MRI for craniofacial measurements. Methods A literature review and expert consensus identified 31 craniofacial biometrics for fetal MRI. An MRI atlas with defined anatomical landmarks served as a template for subject registration, auto-labelling, and biometric calculation. We assessed 108 healthy controls and 24 fetuses with Down syndrome (T21) in the third trimester (29–36 weeks gestational age, GA) to identify meaningful biometrics in T21. Reliability and reproducibility were evaluated in 10 random datasets by four observers. Results Automated labels were produced for all 132 subjects with a 0.3% placement error rate. Seven measurements, including anterior base of skull length and maxillary length, showed significant differences with large effect sizes between T21 and control groups (ANOVA, p<0.001). Manual measurements took 25–35 minutes per case, while automated extraction took approximately 5 minutes. Bland-Altman plots showed agreement within manual observer ranges except for mandibular width, which had higher variability. Extended GA growth charts (19–39 weeks), based on 280 control fetuses, were produced for future research. Conclusion This is the first automated atlas-based protocol using 3D SVR MRI for fetal craniofacial biometrics, accurately revealing morphological craniofacial differences in a T21 cohort. Future work should focus on improving measurement reliability, larger clinical cohorts, and technical advancements, to enhance prenatal care and phenotypic characterisation.

UR - http://www.scopus.com/inward/record.url?scp=85213714764&partnerID=8YFLogxK

U2 - 10.1371/journal.pdig.0000663

DO - 10.1371/journal.pdig.0000663

M3 - Article

AN - SCOPUS:85213714764

SN - 2767-3170

VL - 3

JO - PLOS digital health

JF - PLOS digital health

IS - 12

M1 - e0000663

ER -

Automated craniofacial biometry with 3D T2w fetal MRI

Abstract

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this