Fully-automated alignment of 3D fetal brain ultrasound to a canonical reference space using multi-task learning

Namburete, Ana I. L.; Xie, Weidi; Yaqub, Mohammad; Zisserman, Andrew; Noble, J. Alison

doi:10.1016/j.media.2018.02.006

Cited by 86 publications

(57 citation statements)

References 30 publications

(36 reference statements)

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“…As a comparison, this distance represents only 2% of the mean occipitofrontal diameter of a brain of 22 weeks of gestation. The mean HD results of 9.05 mm is within what was expected [8]. Original: the three mid-planes of the original volume.…”

Section: Discussionsupporting

confidence: 80%

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Automated Fetal Brain Extraction from Clinical Ultrasound Volumes Using 3D Convolutional Neural Networks

Moser

Huang

Papageorghiou

et al. 2020

Communications in Computer and Information Science

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To improve the performance of most neuroimiage analysis pipelines, brain extraction is used as a fundamental first step in the image processing. But in the case of fetal brain development, there is a need for a reliable US-specific tool. In this work we propose a fully automated 3D CNN approach to fetal brain extraction from 3D US clinical volumes with minimal preprocessing. Our method accurately and reliably extracts the brain regardless of the large data variation inherent in this imaging modality. It also performs consistently throughout a gestational age range between 14 and 31 weeks, regardless of the pose variation of the subject, the scale, and even partial feature-obstruction in the image, outperforming all current alternatives.

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Section: Discussionsupporting

confidence: 80%

“…Network results against method from[8]. Our network shows consistently better results accross all evaluations.…”

mentioning

confidence: 70%

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Automated Fetal Brain Extraction from Clinical Ultrasound Volumes Using 3D Convolutional Neural Networks

Moser

Huang

Papageorghiou

et al. 2020

Communications in Computer and Information Science

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“…The results show that the system performs significantly better than a medical researcher in the first and second trimester, but it is still required to obtain the 2D standard plane. Other work in literature focuses on aiding less skilled sonographers in obtaining the 2D standard plane, or reconstructing the 2D standard plane from a 3D volume [ 27 – 32 ]. Combining these methods with our proposed system could further improve inter-observer variability, but this is out of the scope of this work.…”

Section: Discussionmentioning

confidence: 99%

Automated measurement of fetal head circumference using 2D ultrasound images

et al. 2018

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In this paper we present a computer aided detection (CAD) system for automated measurement of the fetal head circumference (HC) in 2D ultrasound images for all trimesters of the pregnancy. The HC can be used to estimate the gestational age and monitor growth of the fetus. Automated HC assessment could be valuable in developing countries, where there is a severe shortage of trained sonographers. The CAD system consists of two steps: First, Haar-like features were computed from the ultrasound images to train a random forest classifier to locate the fetal skull. Secondly, the HC was extracted using Hough transform, dynamic programming and an ellipse fit. The CAD system was trained on 999 images and validated on an independent test set of 335 images from all trimesters. The test set was manually annotated by an experienced sonographer and a medical researcher. The reference gestational age (GA) was estimated using the crown-rump length measurement (CRL). The mean difference between the reference GA and the GA estimated by the experienced sonographer was 0.8 ± 2.6, −0.0 ± 4.6 and 1.9 ± 11.0 days for the first, second and third trimester, respectively. The mean difference between the reference GA and the GA estimated by the medical researcher was 1.6 ± 2.7, 2.0 ± 4.8 and 3.9 ± 13.7 days. The mean difference between the reference GA and the GA estimated by the CAD system was 0.6 ± 4.3, 0.4 ± 4.7 and 2.5 ± 12.4 days. The results show that the CAD system performs comparable to an experienced sonographer. The presented system shows similar or superior results compared to systems published in literature. This is the first automated system for HC assessment evaluated on a large test set which contained data of all trimesters of the pregnancy.

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“…Prior work on slice-to-volume registration in fetal MRI has shown a strong need for regularization and initialization of slice transformations through hierarchical registration [23], [24] or state-space motion modeling [25]. Learning-based methods have been recently used to improve prediction of slice locations in fetal MRI [26], [27] and fetal ultrasound [28]. In [26], [27] anchor-point slice parametrization was used along with the Euclidean loss function based on [29] to predict slice positions and reconstruct fetal MRI in canonical space.…”

Section: Contributionsmentioning

confidence: 99%

Real-Time Deep Pose Estimation With Geodesic Loss for Image-to-Template Rigid Registration

Salehi

Khan

Erdoğmuş

et al. 2019

IEEE Trans. Med. Imaging

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With an aim to increase the capture range and accelerate the performance of state-of-the-art inter-subject and subject-totemplate 3D rigid registration, we propose deep learning-based methods that are trained to find the 3D position of arbitrarily oriented subjects or anatomy in a canonical space based on slices or volumes of medical images. For this, we propose regression convolutional neural networks (CNNs) that learn to predict the angle-axis representation of 3D rotations and translations using image features. We use and compare mean square error and geodesic loss to train regression CNNs for 3D pose estimation used in two different scenarios: slice-to-volume registration and volume-to-volume registration. As an exemplary application, we applied the proposed methods to register arbitrarily oriented reconstructed images of fetuses scanned in-utero at a wide gestational age range to a standard atlas space. Our results show that in such registration applications that are amendable to learning, the proposed deep learning methods with geodesic loss minimization achieved 3D pose estimation with a wide capture range in real-time (< 100ms). We also tested the generalization capability of the trained CNNs on an expanded age range and on images of newborn subjects with similar and different MR image contrasts. We trained our models on T2-weighted fetal brain MRI scans and used them to predict the 3D pose of newborn brains based on T1-weighted MRI scans. We showed that the trained models generalized well for the new domain when we performed image contrast transfer through a conditional generative adversarial network. This indicates that the domain of application of the trained deep regression CNNs can be further expanded to image modalities and contrasts other than those used in training. A combination of our proposed methods with accelerated optimization-based registration algorithms can dramatically enhance the performance of automatic imaging devices and image processing methods of the future.

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Fully-automated alignment of 3D fetal brain ultrasound to a canonical reference space using multi-task learning

Cited by 86 publications

References 30 publications

Automated Fetal Brain Extraction from Clinical Ultrasound Volumes Using 3D Convolutional Neural Networks

Automated Fetal Brain Extraction from Clinical Ultrasound Volumes Using 3D Convolutional Neural Networks

Automated measurement of fetal head circumference using 2D ultrasound images

Real-Time Deep Pose Estimation With Geodesic Loss for Image-to-Template Rigid Registration

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