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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Journal ArticleDOI
A Deep Convolutional Neural Network-Based Framework for Automatic Fetal Facial Standard Plane Recognition
TL;DR: A method to automatically recognize fetal facial standard plane via a deep convolutional neural network (DCNN) architecture is proposed and the advantage of the proposed method over traditional approaches and the effectiveness of DCNN to recognize FFSP for clinical diagnosis is demonstrated.
Book ChapterDOI
Towards Automatic Semantic Segmentation in Volumetric Ultrasound
TL;DR: This work proposes the first and fully automatic framework for the simultaneous segmentation of multiple objects, including fetus, gestational sac and placenta, in ultrasound volumes, which remains as a rarely-studied but great challenge.
Journal ArticleDOI
Quantitative ultrasound texture analysis of fetal lungs to predict neonatal respiratory morbidity.
Elisenda Bonet-Carne,Montserrat Palacio,Teresa Cobo,Alvaro Perez-Moreno,Marta López,J. P. Piraquive,J. C. Ramirez,Francesc Botet,Fernando O. Marques,Eduard Gratacós +9 more
TL;DR: To develop and evaluate the performance of a novel method for predicting neonatal respiratory morbidity based on quantitative analysis of the fetal lung by ultrasound.
Proceedings ArticleDOI
Real-Time Automatic Fetal Brain Extraction in Fetal MRI by Deep Learning
Seyed Sadegh Mohseni Salehi,Seyed Raein Hashemi,Clemente Velasco-Annis,Abdelhakim Ouaalam,Judy A. Estroff,Deniz Erdogmus,Simon K. Warfield,Ali Gholipour +7 more
TL;DR: In this paper, a 2D U-Net and autocontext based segmentation method was proposed to segment the fetal brain in real-time while the fetal MRI slices are being acquired.
Journal ArticleDOI
Slic-Seg: A minimally interactive segmentation of the placenta from sparse and motion-corrupted fetal MRI in multiple views.
Guotai Wang,Maria A. Zuluaga,Rosalind Pratt,Michael Aertsen,Tom Doel,Maria Klusmann,Anna L. David,Jan Deprest,Tom Vercauteren,Sebastien Ourselin +9 more
TL;DR: Minimal user interaction is needed for a good segmentation of the placenta and co-segmentation of multiple volumes outperforms single sparse volume based method.