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: The necessary steps of technical evolution from the initial open fetal surgery approach until the development of minimally invasive techniques of fetal endoscopic surgery (fetoscopy) are discussed.
Book ChapterDOI
A Constrained Regression Forests Solution to 3D Fetal Ultrasound Plane Localization for Longitudinal Analysis of Brain Growth and Maturation
Mohammad Yaqub,Anil Kopuri,Anil Kopuri,Sylvia Rueda,Peter B. Sullivan,Kenneth McCormick,Kenneth McCormick,J. Alison Noble +7 more
TL;DR: A method based on regression forests (RF) with important algorithm design considerations taken into account to provide an accurate plane-finding solution to allow image-based biomarkers to be tracked from pre-birth through the first weeks of post-birth life.
Journal ArticleDOI
Advances and applications in fetal magnetic resonance imaging
Lisa Story,Mary A. Rutherford +1 more
TL;DR: Improvements in acquisition and post‐acquisition software that enable brain structures to be evaluated in greater detail, allowing the assessment of both normal and abnormal development, are discussed.
Proceedings ArticleDOI
Characterization of ultrasonic images of the placenta based on textural features
TL;DR: The task of classifying ultrasonic images of the placenta according with the gradation proposed by Grannum (1979) is attempted and the ability of a decision tree classifier to discriminate different textures with three sets of textural features was tested.
Proceedings ArticleDOI
Fetal echocardiographic image segmentation using neural networks
TL;DR: This paper discusses supervised and unsupervised neural networks approaches to fetal echocardiographic image segmentation and obtained results that showed sufficient details of the internal heart anatomy to allow medical diagnosis.