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
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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Proceedings ArticleDOI
Where is my baby? A fast fetal head auto-alignment in 3D-ultrasound
Rémi Cuingnet,Oudom Somphone,Benoit Mory,Raphael Prevost,Mohammad Yaqub,Raffaele Napolitano,Aris T. Papageorghiou,David N. Roundhill,J.A. Noble,Roberto Ardon +9 more
TL;DR: The method presented in this paper aims at helping the clinician navigate through the brain by automatically aligning the head in near real time (<; 1 s) in a 3D ultrasound volume.
Proceedings ArticleDOI
Automatic Brain Extraction in Fetal MRI using Multi-Atlas-based Segmentation
Sébastien Tourbier,Patric Hagmann,Maud Cagneaux,Laurent Guibaud,Subrahmanyam Gorthi,Marie Schaer,Jean-Philippe Thiran,Reto Meuli,Meritxell Bach Cuadra +8 more
TL;DR: This work assesses on a large dataset the performance of Multiple Atlas Fusion (MAF) strategies to automatically address the problem of manual brain segmentation in fetal brain MRI and shows that MAF significantly increase the accuracy ofbrain segmentation as regards single-atlas strategy.
Journal ArticleDOI
Extralobar pulmonary sequestration in neonates: The natural course and predictive factors associated with spontaneous regression.
Hee Mang Yoon,Ellen Ai-Rhan Kim,Sung-Hoon Chung,Seon-Ok Kim,Ah Young Jung,Young Ah Cho,Chong Hyun Yoon,Jin Seong Lee +7 more
TL;DR: The volume and diameter of systemic feeding arteries of EPS spontaneously decreased within 4 years without treatment, and EPSs showing a low tissue attenuation and small diameter of the largest systemic feeding artery on initial contrast-enhanced CT scans were likely to regress spontaneously.
Proceedings ArticleDOI
Automated detection of fetal cardiac structure from first-trimester ultrasound sequences
TL;DR: A novel method is proposed for the automated detection of the fetal cardiac structure from first-trimester ultrasound images by simultaneously suppressing the speckle noise according to its statistical property and emphasize the motion information which is important for the next detection.
Journal ArticleDOI
Discriminative Learning for Automatic Staging of Placental Maturity via Multi-layer Fisher Vector.
TL;DR: A method to automatically stage placenta maturity from B-mode ultrasound (US) images based on dense sampling and novel feature descriptors and a multi-layer FV is investigated to exploit the spatial information rather than the single layer FV.