Robust regression of brain maturation from 3D fetal neurosonography using CRNs

Conference item

We propose a fully three-dimensional Convolutional Regression Network (CRN) for the task of predicting fetal brain maturation from 3D ultrasound (US) data. Anatomical development is modelled as the sonographic patterns visible in the brain at a given gestational age, which are aggregated by the model into a single value: the brain maturation (BM) score. These patterns are learned from 589 3D fetal volumes, and the model is applied to 3D US images of 146 fetal subjects acquired at multiple, ethnically diverse sites, spanning an age range of 18 to 36 gestational weeks. Achieving a mean error of 7.7 days between ground-truth and estimated maturational scores, our method outperforms the current state-of-art for automated BM estimation from 3D US images.

Authors: Namburete, A; Xie, W; Noble, J

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Springer, Cham
• Host title: OMIA 2017, FIFI 2017: Fetal, Infant and Ophthalmic Medical Image Analysis
• Journal: OMIA 2017, FIFI 2017: Fetal, Infant and Ophthalmic Medical Image Analysis
• Volume: 10554
• Pages: 73-80
• Series: Lecture Notes in Computer Science
• Publication date: 2017-09-09
• Acceptance date: 2017-07-08
• DOI: 10.1007/978-3-319-67561-9_8
• ISSN: 0302-9743
• ISBN: 9783319675602