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
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
Citations
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Journal ArticleDOI
CA-Net: Comprehensive Attention Convolutional Neural Networks for Explainable Medical Image Segmentation
Ran Gu,Guotai Wang,Tao Song,Rui Huang,Michael Aertsen,Jan Deprest,Sebastien Ourselin,Tom Vercauteren,Shaoting Zhang +8 more
TL;DR: CA-Net as mentioned in this paper proposes a joint spatial attention module to make the network focus more on the foreground region and a novel channel attention module is proposed to adaptively recalibrate channel-wise feature responses and highlight the most relevant feature channels.
Journal ArticleDOI
CA-Net: Comprehensive Attention Convolutional Neural Networks for Explainable Medical Image Segmentation
Ran Gu,Guotai Wang,Tao Song,Rui Huang,Michael Aertsen,Jan Deprest,Sebastien Ourselin,Tom Vercauteren,Shaoting Zhang +8 more
TL;DR: This work makes extensive use of multiple attentions in a CNN architecture and proposes a comprehensive attention-based CNN (CA-Net) for more accurate and explainable medical image segmentation that is aware of the most important spatial positions, channels and scales at the same time.
Journal ArticleDOI
A New Model for Brain Tumor Detection Using Ensemble Transfer Learning and Quantum Variational Classifier
Javeria Amin,Muhammad Almas Anjum,Muhammad Sharif,Saima Jabeen,Seifedine Kadry,Pablo Moreno Ger +5 more
TL;DR: Deep features are extracted from the inceptionv3 model, in which score vector is acquired from softmax and supplied to the quantum variational classifier (QVR) for discrimination between glioma, meningiomas, no tumor, and pituitary tumor to prove the proposed model's effectiveness.
Journal ArticleDOI
RLDS: An explainable residual learning diagnosis system for fetal congenital heart disease
TL;DR: Wang et al. as discussed by the authors 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
Accurate Detection of Septal Defects With Fetal Ultrasonography Images Using Deep Learning-Based Multiclass Instance Segmentation
Siti Nurmaini,Muhammad Naufal Rachmatullah,Ade Iriani Sapitri,Annisa Darmawahyuni,Adithia Jovandy,Firdaus Firdaus,Bambang Tutuko,Rossi Passarella +7 more
TL;DR: The results suggest that the model used has a high potential to help cardiologists complete the initial screening for fetal congenital heart disease and a strong correlation between the predicted septal defects and ground truth as a mean average precision (mAP).
References
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Journal ArticleDOI
DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation
Guotai Wang,Maria A. Zuluaga,Wenqi Li,Rosalind Pratt,Premal A. Patel,Michael Aertsen,Tom Doel,Anna L. David,Jan Deprest,Sebastien Ourselin,Tom Vercauteren +10 more
TL;DR: A deep learning-based interactive segmentation method to improve the results obtained by an automatic CNN and to reduce user interactions during refinement for higher accuracy, and obtains comparable and even higher accuracy with fewer user interventions and less time compared with traditional interactive methods.
Journal ArticleDOI
DeepCut: Object Segmentation From Bounding Box Annotations Using Convolutional Neural Networks
Martin Rajchl,Matthew C. H. Lee,Ozan Oktay,Konstantinos Kamnitsas,Jonathan Passerat-Palmbach,Wenjia Bai,Mellisa Damodaram,Mary A. Rutherford,Joseph V. Hajnal,Bernhard Kainz,Daniel Rueckert +10 more
TL;DR: DeepCut as discussed by the authors proposes a method to obtain pixelwise object segmentations given an image dataset labeled weak annotations, in our case bounding boxes, by training a neural network classifier from bounding box annotations.
Journal ArticleDOI
Standard Plane Localization in Fetal Ultrasound via Domain Transferred Deep Neural Networks
TL;DR: This paper presents a learning-based approach to locate the fetal abdominal standard plane (FASP) in US videos by constructing a domain transferred deep convolutional neural network (CNN) that outperforms the state-of-the-art method for the FASP localization.
Journal ArticleDOI
Fetal responses to placental insufficiency: an update
TL;DR: The aim of this review is to illustrate the multisystem fetal effects of placental insufficiency and how this information may stimulate future research.
Journal ArticleDOI
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,Mary E. D'Alton,Diana L. Farmer +13 more
TL;DR: Myelomeningocele is one of the most common birth defects, characterized by a defect in the bony spine and resultant extrusion of the spinal cord into a sac filled with cerebrospinal fluid.