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

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 mode...

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Autori principali: Namburete, A, Xie, W, Noble, J
Natura: Conference item
Pubblicazione: Springer, Cham 2017
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author Namburete, A
Xie, W
Noble, J
author_facet Namburete, A
Xie, W
Noble, J
author_sort Namburete, A
collection OXFORD
description 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.
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spelling oxford-uuid:45acfec8-2eaa-4ebc-8ce3-f19cc7f95cb92022-03-26T15:09:11ZRobust regression of brain maturation from 3D fetal neurosonography using CRNsConference itemhttp://purl.org/coar/resource_type/c_5794uuid:45acfec8-2eaa-4ebc-8ce3-f19cc7f95cb9Symplectic Elements at OxfordSpringer, Cham2017Namburete, AXie, WNoble, JWe 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.
spellingShingle Namburete, A
Xie, W
Noble, J
Robust regression of brain maturation from 3D fetal neurosonography using CRNs
title Robust regression of brain maturation from 3D fetal neurosonography using CRNs
title_full Robust regression of brain maturation from 3D fetal neurosonography using CRNs
title_fullStr Robust regression of brain maturation from 3D fetal neurosonography using CRNs
title_full_unstemmed Robust regression of brain maturation from 3D fetal neurosonography using CRNs
title_short Robust regression of brain maturation from 3D fetal neurosonography using CRNs
title_sort robust regression of brain maturation from 3d fetal neurosonography using crns
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