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
Congenital anomalies: Case definition and guidelines for data collection, analysis, and presentation of immunization safety data.
Malini B. DeSilva,Flor M. Munoz,Mark McMillan,Alison Tse Kawai,Helen Marshall,Kristine Macartney,Jyoti Joshi,Martina Oneko,Annette Elliott Rose,Helen Dolk,Francesco Trotta,Hans M. L. Spiegel,Sylvie Tomczyk,Anju Shrestha,Sonali Kochhar,Elyse O. Kharbanda +15 more
TL;DR: The Brighton Collaboration Congenital Anomalies Working Group’s case definition and guidelines for data ollection, analysis, and presentation of immunization safety data are published.
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Prenatal screening for structural congenital heart disease
TL;DR: Evidence supports the use of structured training interventions and feedback to those undertaking sonographic examinations, to improve the prenatal detection of congenital heart disease.
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In Vivo Quantification of Placental Insufficiency by BOLD MRI: A Human Study
Jie Luo,Jie Luo,Esra Abaci Turk,Esra Abaci Turk,Carolina Bibbo,Borjan Gagoski,Drucilla J. Roberts,Mark Vangel,Clare M. Tempany-Afdhal,Carol E. Barnewolt,Judy A. Estroff,Arvind Palanisamy,William H. Barth,Chloe Zera,Norberto Malpica,Norberto Malpica,Polina Golland,Elfar Adalsteinsson,Julian N. Robinson,Patricia Ellen Grant +19 more
TL;DR: The potential of BOLD MRI with maternal hyperoxia to quantify regional placental function in vivo is demonstrated and is demonstrated to be positive with respect to placental pathology.
Journal ArticleDOI
Segmentation of Fetal Left Ventricle in Echocardiographic Sequences Based on Dynamic Convolutional Neural Networks
TL;DR: A dynamic convolutional neural networks based on multiscale information and fine-tuning is proposed for fetal LV segmentation and results show that the segmentation results agree well with the ground truth, especially in the cases with leakage, blurry boundaries, and subject-to-subject variations.
Journal ArticleDOI
Three-dimensional sonographic calculation of the volume of intracranial structures in growth-restricted and appropriate-for-gestational age fetuses.
Andrés Benavides-Serralde,Edgar Hernandez-Andrade,J. G. Fernandez-Delgado,Walter Plasencia,Matthias Scheier,Fatima Crispi,Francesc Figueras,Kypros H. Nicolaides,Eduard Gratacós +8 more
TL;DR: To evaluate the feasibility and reproducibility of volume segmentation of fetal intracranial structures using three‐dimensional ultrasound imaging, and to estimate differences in the volume of intrac Cranial structures between intrauterine growth‐restricted and appropriate‐for‐gestational age fetuses, 3D ultrasound imaging is used.