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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Proceedings ArticleDOI
Automatic segmentation of the fetal cerebellum using spherical harmonics and gray level profiles
TL;DR: A new automatic scheme for the segmentation of the 3D surface of the cerebellum in ultrasound volumes is developed, using a spherical harmonics model and the optimization of an objective function based on gray level voxel profiles, to help with the annotation in the evaluation of cerebellar diameter.
Journal ArticleDOI
Thickness Based Characterization of Ultrasound Placenta Images Using Regression Analysis
G. Malathi,V. Shanthi +1 more
TL;DR: This pilot study involves the feasibility for classifying the ultrasound images ofplacenta with complicating diabetes based on placenta thickness using statistical textural features.
Journal ArticleDOI
Automatic Segmentation of the Cerebellum in Ultrasound Volumes of the Fetal Brain
G. Velásquez Rodríguez,F. Arámbula Cosío,M.E. Guzmán Huerta,L. Camargo Marín,H. Borboa Olivares,Boris Escalante Ramírez +5 more
TL;DR: An automatic scheme for the segmentation of the 3D surface of the cerebellum in ultrasound volumes, using a spherical harmonics model is developed, which shows potential to eectively assist the experts in the assessment of fetal growth in ultrasoundvolume.
Journal ArticleDOI
2D/3D fetal cardiac dataset segmentation using a deformable model
TL;DR: A level set deformable model to automatically delineate the small fetal cardiac chambers and compares automatic and manual tracings to a physical phantom and also measures inter observer variation.
Book ChapterDOI
Review on Advanced Techniques in 2-D Fetal Echocardiography: An Image Processing Perspective
TL;DR: This chapter provides a review of 2-D fetal echocardiography techniques in image processing point of view and concentrates on three main aspects to support diagnosis in 2- D fetal e chocardiographic: speckle reduction, image segmentation, and image analysis.