Fetal face shape analysis from prenatal 3D ultrasound images

© 2024. The Author(s)..

3D ultrasound imaging of fetal faces has been predominantly confined to qualitative assessment. Many genetic conditions evade diagnosis and identification could assist with parental counselling, pregnancy management and neonatal care planning. We describe a methodology to build a shape model of the third trimester fetal face from 3D ultrasound and show how it can objectively describe morphological features and gestational-age related changes of normal fetal faces. 135 fetal face 3D ultrasound volumes (117 appropriately grown, 18 growth-restricted) of 24-34 weeks gestation were included. A 3D surface model of each face was obtained using a semi-automatic segmentation workflow. Size normalisation and rescaling was performed using a growth model giving the average size at every gestation. The model demonstrated a similar growth rate to standard head circumference reference charts. A landmark-free morphometry model was estimated to characterize shape differences using non-linear deformations of an idealized template face. Advancing gestation is associated with widening/fullness of the cheeks, contraction of the chin and deepening of the eyes. Fetal growth restriction is associated with a smaller average facial size but no morphological differences. This model may eventually be used as a reference to assist in the prenatal diagnosis of congenital anomalies with characteristic facial dysmorphisms.

Medienart:

E-Artikel

Erscheinungsjahr:

2024

Erschienen:

2024

Enthalten in:

Zur Gesamtaufnahme - volume:14

Enthalten in:

Scientific reports - 14(2024), 1 vom: 22. Feb., Seite 4411

Sprache:

Englisch

Beteiligte Personen:

Sivera, Raphael [VerfasserIn]
Clark, Anna E [VerfasserIn]
Dall'Asta, Andrea [VerfasserIn]
Ghi, Tullio [VerfasserIn]
Schievano, Silvia [VerfasserIn]
Lees, Christoph C [VerfasserIn]

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Themen:

Journal Article

Anmerkungen:

Date Completed 26.02.2024

Date Revised 26.02.2024

published: Electronic

Citation Status MEDLINE

doi:

10.1038/s41598-023-50386-9

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM368785076