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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Book ChapterDOI
Interactive Segmentation and Analysis of Fetal Ultrasound Images
TL;DR: This work explores the use of two algorithms, region growing and a variant of split/merge algorithm for segmenting sequences of fetal ultrasound images, and describes an interactive system that can rapidly process and segment an arbitrary number of features.
Proceedings ArticleDOI
Automatic segmentation of low resolution fetal cardiac data using snakes with shape priors
TL;DR: This paper presents a level set deformable model to segment all four chambers of the fetal heart simultaneously and shows its results in 2D on 53 images taken from only 8 datasets.
Proceedings ArticleDOI
Fetal lung segmentation using texture-based boundary enhancement and active contour models
TL;DR: A novel method using the texture-based boundary enhancement and active contour models is developed to semi-automatically segment the fetal lung from fetal chest ultrasound images.
Proceedings ArticleDOI
Learning-based scan plane identification from fetal head ultrasound images
Xiaoming Liu,Pavan Annangi,Mithun Das Gupta,Bing Yu,Dirk R. Padfield,Jyotirmoy Banerjee,Kajoli Banerjee Krishnan +6 more
TL;DR: A novel automated learning-based algorithm is presented to identify an acceptable fetal head scan plane that uses the state-of- the-art Active Appearance Models techniques from the image processing and computer vision literature and ties them to presence or absence of the inclusions within the head to automatically compute a score to represent the goodness of a scan plane.
Proceedings ArticleDOI
Cardiac Structure Recognition in Ultrasound Images
TL;DR: The paper presents a research on detection of cardiac structures in echocardiography gray images from fetal hearts using pattern recognition and a density probability function of scales of gray to define the choice of the structures of interest.