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
Placenta Maps: In Utero Placental Health Assessment of the Human Fetus
Haichao Miao,Gabriel Mistelbauer,Alexey Karimov,Amir Alansary,Alice Davidson,David F. A. Lloyd,M. Damodaram,Lisa Story,Jana Hutter,Joseph V. Hajnal,Mary A. Rutherford,Bernhard Preim,Bernhard Kainz,M. Eduard Gröller +13 more
TL;DR: A novel visualization technique is introduced that presents the fetal and maternal side of the placenta in a standardized way and establishes the basis for a comparative assessment of multiple placentas to analyze possible pathologic arrangements and to support the research and understanding of this vital organ.
Journal ArticleDOI
ANFIS based decision support system for prenatal detection of Truncus Arteriosus congenital heart defect
S. Sridevi,S. Nirmala +1 more
TL;DR: This proposed CADSS is the first framework implemented to diagnose the prenatal Truncus Arteriosus congenital heart defect (TACHD) from 2D US images and proves that the proposed classifier has the potential to produce the higher classification accuracy than existing classifiers.
Journal ArticleDOI
Systems biology approaches to identify developmental bases for lung diseases.
TL;DR: A greater understanding of the regulatory processes contributing to lung development could be helpful to identify strategies to ameliorate morbidity and mortality in premature infants and to identify individuals at risk for congenital and/or chronic lung diseases.
Book ChapterDOI
Slic-Seg: Slice-by-Slice Segmentation Propagation of the Placenta in Fetal MRI Using One-Plane Scribbles and Online Learning
Guotai Wang,Maria A. Zuluaga,Rosalind Pratt,Michael Aertsen,Anna L. David,Jan Deprest,Tom Vercauteren,Sebastien Ourselin +7 more
TL;DR: A minimally interactive online learning-based method named Slic-Seg to obtain accurate placenta segmentations from MRI that has a high performance in the start slice even in cases where sparse scribbles provided by the user lead to poor results with the competitive approaches, and has a robust segmentation in subsequent slices.
Proceedings ArticleDOI
Automatic detection of local fetal brain structures in ultrasound images
TL;DR: An automatic technique to locate four local fetal brain structures in 3D ultrasound images based on a discriminative model (Random Forests) which is gaining a lot of interest recently is proposed.