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Journal ArticleDOI

Segmentation and classification in MRI and US fetal imaging: Recent trends and future prospects.

TLDR
This review covers state‐of‐the‐art segmentation and classification methodologies for the whole fetus and, more specifically, the fetal brain, lungs, liver, heart and placenta in magnetic resonance imaging and (3D) ultrasound for the first time.
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This article is published in Medical Image Analysis.The article was published on 2019-01-01. It has received 70 citations till now.

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Citations
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Journal ArticleDOI

Magnetic Resonance Prediction of Lung Maturity in Fetuses With Congenital Diaphragmatic Hernia.

TL;DR: In this paper, the lung to liver MR T2 signal ratio was not predictive of neonatal outcome in fetuses with congenital diaphragmatic hernia (CDH).
Book ChapterDOI

Contour Dice Loss for Structures with Fuzzy and Complex Boundaries in Fetal MRI

TL;DR: In this article , a new formulation of the Dice loss was proposed for placenta segmentation based on the Contour Dice metric, which is computed efficiently for each slice via erosion, dilation and XOR operators.
Journal ArticleDOI

Ultrasonic evaluation of fetal lung development using deep learning with graph

TL;DR: In this paper , the authors proposed a deep learning model for automated fetal lung segmentation and measurement, which was constructed combined U-Net with Graph model and pre-trained Vgg-16 network.
Journal ArticleDOI

Improved differentiation between hypo/hypertelorism and normal fetuses based on MRI using automatic ocular biometric measurements, ocular ratios, and machine learning multi-parametric classification

TL;DR: A fully automatic method for computing fetal ocular biometry from MRI is proposed, achieving high performance, comparable to that of an expert fetal neuro-radiologist, and two new parameters, IOD-ratio and BOD-Ratio, are proposed for routine clinical use in ultrasound and MRI.
Journal ArticleDOI

Tunicate swarm-based grey wolf algorithm for fetal heart chamber segmentation and classification: a heuristic-based optimal feature selection concept

TL;DR: Extensive performance result shows that proposed intelligent techniques performs better than the existing segmentation methods.
References
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Journal ArticleDOI

Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation

TL;DR: An efficient and effective dense training scheme which joins the processing of adjacent image patches into one pass through the network while automatically adapting to the inherent class imbalance present in the data, and improves on the state-of-the‐art for all three applications.
Journal ArticleDOI

Interactive Medical Image Segmentation Using Deep Learning With Image-Specific Fine Tuning

TL;DR: A novel deep learning-based interactive segmentation framework by incorporating CNNs into a bounding box and scribble-based segmentation pipeline and proposing a weighted loss function considering network and interaction-based uncertainty for the fine tuning is proposed.
Journal ArticleDOI

The ultrasonic changes in the maturing placenta and their relation to fetal pulmonic maturity.

TL;DR: A correlation between maturational changes of the placenta as seen by ultrasound and fetal pulmonic maturity as indicated by L/S ratio is suggested.
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