Fully-automated alignment of 3D fetal brain ultrasound to a canonical reference space using multi-task learning
Open accessAbstract
Methods for aligning 3D fetal neurosonography images must be robust to (i) intensity variations, (ii) anatomical and age-specific differences within the fetal population, and (iii) the variations in fetal position. To this end, we propose a multi-task fully convolutional neural network (FCN) architecture to address the problem of 3D fetal brain localization, structural segmentation, and alignment to a referential coordinate system. Instead of treating these tasks as independent problems, we optimize the network by simultaneously learning features shared within the input data pertaining to the correlated tasks, and later branching out into task-specific output streams. Brain alignment is achieved by defining a parametric coordinate system based on skull boundaries, location of the eye sockets, and head pose, as predicted from intracranial structures. This information is used to estimate an affine transformation to align a volumetric image to the skull-based coordinate system. Co-alignment of 140 fetal ultrasound volumes (age range: 26.0 ± 4.4 weeks) was achieved with high brain overlap and low eye localization error, regardless of gestational age or head size. The automatically co-aligned volumes show good structural correspondence between fetal anatomies.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Yeung et al., Namburete et al., Yu et al., and Di Vece et al. proposed a plane localization system as a 3D reference space for locating 2D planes to improve acquisition of standard planes and facilitate anatomical orientation.
“The establishment of a plane localization system as a 3D reference space for locating 2D planes was proposed by Yeung et al., Namburete et al., Yu et al., and Di Vece et al. for improving the acquisition of standard planes and facilitating anatomical orientation for sonographers [78,80,82,83].”
Ossification of the fetal skull provoked an increase in the shadowing of US images and thus reduced image quality and visibility.
“Ossification of the fetal skull provoked an increase in the shadowing of US images and thus reduced image quality and visibility [80].”
A limitation of AI US imaging in fetal neurosonography was the problem of miscalculations when US images were not in accordance with the guidelines for standard planes.
“and the problem of miscalculations when US images were not in accordance with the guidelines for standard planes [6,80].”
The primary limitation of AI US imaging in fetal neurosonography was rapid anatomical development of fetal brain structures due to brain maturation, increasing head size and degree of ossification with rising GA.
“The primary limitation of AI US imaging in this topic was described to be the rapid anatomical development of fetal brain structures due to brain maturation, increasing head size and degree of ossification with rising GA [78,80,84].”
The citation lists haven’t been fetched yet
References 51 · Cited by 95
- Authors
- Namburete, A. I. L., Xie, W., Yaqub, M., Zisserman, A., & Noble, J. A.
- Affiliations
- University of Oxford (GB)
- Corresponding author
- Namburete, Ana I.L.
- Xie, Weidi
- Venue
- Medical Image Analysis
- Publisher
- Elsevier BV
- Published
- 2018-05
- Volume
- 46
- Pages
- 1-14
- ISSN
- 1361-8415 (ISSN-L)
- OpenAlex
- W2793117765
- Cited by
- 95
- References
- 51
- Citations by year
- 2026: 4 · 2025: 8 · 2024: 10 · 2023: 19 · 2022: 19 · 2021: 8 · 2020: 13 · 2019: 6 · 2018: 7 · 2017: 1
- FWCI
- 4.21
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (am, 303 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Medical Image Analysis (acceptedVersion)
- PubMed (publishedVersion)
- Funders
- Bill and Melinda Gates Foundation (OPP1128941)
- UK Royal Academy of Engineering
- UK Engineering and Physical Sciences Research Council (EPSRC) Programme (EP/M013774/1)
- Innovate UK (101684)
- UK EPSRC (EP/L505316/1)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Fetal and Pediatric Neurological Disorders
- Life Sciences › Neuroscience › Neurology › Brain Tumor Detection and Classification
- Keywords
- fetal brain ultrasound, image registration, multi-task learning, fully convolutional network, brain segmentation, affine transformation, head pose estimation, gestational age
- MeSH terms
- Adult, Algorithms, Brain, Female, Gestational Age, Humans, Image Processing, Computer-Assisted, Pregnancy, Ultrasonography, Prenatal, Neural Networks, Computer, Imaging, Three-Dimensional, Neuroimaging
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
