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
Advances in fetal surgery.
TL;DR: Rapid advances in imaging and instrumentation technology combined with superior knowledge of fetal pathophysiology has led to the development of novel intrauterine interventions for most common fetal anomalies.
Book ChapterDOI
Automated localization of fetal organs in MRI using random forests with steerable features
Kevin Keraudren,Bernhard Kainz,Ozan Oktay,Vanessa Kyriakopoulou,Mary A. Rutherford,Joseph V. Hajnal,Daniel Rueckert +6 more
TL;DR: A method based on Random Forests with steerable features to automatically localize the heart, lungs and liver in fetal MRI and can be used to initialize subsequent processing steps such as segmentation or motion correction, as well as automatically orient the 3D volume based on the fetal anatomy to facilitate clinical examination.
Journal ArticleDOI
Automatic segmentation of the fetal cerebellum on ultrasound volumes, using a 3D statistical shape model.
Benjamín Gutiérrez-Becker,Fernando Arámbula Cosío,Mario Guzmán Huerta,Jesus Andres Benavides-Serralde,Lisbeth Camargo-Marín,Verónica Medina Bañuelos +5 more
TL;DR: This work presents an algorithm based on a 3D statistical shape model to segment the fetal cerebellum on 3D ultrasound volumes using an ad hoc objective function which is in turn optimized using the Nelder–Mead simplex algorithm.
Journal ArticleDOI
Prenatally diagnosed congenital diaphragmatic hernia: optimal mode of delivery?
Carmen Mesas Burgos,Björn Frenckner,Matías Luco,Matthew T. Harting,Pamela A. Lally,Kevin P. Lally +5 more
TL;DR: There was no difference in outcome between the different delivery modes at similar gestational age (GA) at birth, with worse outcomes at lower GA, and the mode nor time of delivery seems to affect the overall outcome for patients with prenatally diagnosed CDH.
Journal ArticleDOI
Comparison of septal defects in 2D and 3D echocardiography using active contour models.
TL;DR: This work will implement an active contour algorithm to automatically extract the endocardial borders of septal defects in echocardiographic images, and compare the size of the defects in the original 2D images and the 3D data sets.