Journal ArticleDOI
Segmentation and classification in MRI and US fetal imaging: Recent trends and future prospects.
Jordina Torrents-Barrena,Gemma Piella,Narcís Masoller,Eduard Gratacós,Elisenda Eixarch,Mario Ceresa,Miguel Ángel González Ballester +6 more
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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.About:
This article is published in Medical Image Analysis.The article was published on 2019-01-01. It has received 70 citations till now.read more
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Journal ArticleDOI
Fetal health classification from cardiotocographic data using machine learning
Abolfazl Mehbodniya,Arokia Jesu Prabhu Lazar,Julian Webber,Dilip Kumar Sharma,Santhosh Jayagopalan,Kousalya K,Pallavi Singh,Regin Rajan,Sharnil Pandya,Sudhakar Sengan +9 more
Journal ArticleDOI
Assessment of Radiomics and Deep Learning for the Segmentation of Fetal and Maternal Anatomy in Magnetic Resonance Imaging and Ultrasound.
Jordina Torrents-Barrena,Núria Monill,Gemma Piella,Eduard Gratacós,Elisenda Eixarch,Mario Ceresa,Miguel Ángel González Ballester +6 more
TL;DR: This work aims to efficiently segment different intrauterine tissues in fetal magnetic resonance imaging (MRI) and 3D ultrasound and suggests that combining the selected 10 radiomic features per anatomy along with DeepLabV3+ or BiSeNet architectures for MRI, and PSPNet or Tiramisu for 3D US, can lead to the highest fetal / maternal tissue segmentation performance, robustness, informativeness, and heterogeneity.
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RLDS: An explainable residual learning diagnosis system for fetal congenital heart disease
TL;DR: Wang et al. as mentioned in this paper proposed a simple yet effective residual learning diagnosis system (RLDS) for diagnosing fetal CHD to improve diagnostic accuracy, which adopts convolutional neural networks to extract discriminative features of the fetal cardiac anatomical structures.
Journal ArticleDOI
Deep Learning-Based Computer-Aided Fetal Echocardiography: Application to Heart Standard View Segmentation for Congenital Heart Defects Detection
Siti Nurmaini,Muhammad Naufal Rachmatullah,Ade Iriani Sapitri,Annisa Darmawahyuni,Bambang Tutuko,Firdaus Firdaus,Radiyati Umi Partan,Nuswil Bernolian +7 more
TL;DR: In this article, a deep learning-based computer-aided fetal heart echocardiography examinations with an instance segmentation approach was proposed, which inherently segments the four standard heart views and detects the defect simultaneously.
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A review of image processing methods for fetal head and brain analysis in ultrasound images
Helena R. Torres,Pedro Morais,Bruno Oliveira,Cahit Birdir,Mario Rüdiger,Jaime C. Fonseca,João L. Vilaça +6 more
TL;DR: A review of state-of-the-art methods for head and brain analysis in ultrasound imaging can be found in this paper , where a total of 109 articles published since 2010 were analyzed.
References
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Journal ArticleDOI
Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation
Konstantinos Kamnitsas,Christian Ledig,Virginia F. J. Newcombe,Joanna P. Simpson,Andrew D. Kane,David K. Menon,Daniel Rueckert,Ben Glocker +7 more
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.
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A Randomized Trial of Prenatal versus Postnatal Repair of Myelomeningocele
N. Scott Adzick,Elizabeth Thom,Catherine Y. Spong,John W. Brock,Pamela K. Burrows,Mark P. Johnson,Lori J. Howell,Jody A. Farrell,Mary E. Dabrowiak,Leslie N. Sutton,Nalin Gupta,Noel Tulipan,Diana L. Farmer +12 more
TL;DR: Prenatal surgery for myelomeningocele reduced the need for shunting and improved motor outcomes at 30 months but was associated with maternal and fetal risks and increased risk of preterm delivery and uterine dehiscence at delivery.
Journal ArticleDOI
Interactive Medical Image Segmentation Using Deep Learning With Image-Specific Fine Tuning
Guotai Wang,Wenqi Li,Maria A. Zuluaga,Rosalind Pratt,Premal A. Patel,Michael Aertsen,Tom Doel,Anna L. David,Jan Deprest,Sebastien Ourselin,Tom Vercauteren +10 more
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.
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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.