Improving the generalizability of convolutional neural network-based segmentation on CMR images
Background: Convolutional neural network (CNN) based segmentation methods provide an
efficient and automated way for clinicians to assess the structure and function of the heart in …
efficient and automated way for clinicians to assess the structure and function of the heart in …
Improving cardiac MRI convolutional neural network segmentation on small training datasets and dataset shift: A continuous kernel cut approach
Cardiac magnetic resonance imaging (MRI) provides a wealth of imaging biomarkers for
cardiovascular disease care and segmentation of cardiac structures is required as a first …
cardiovascular disease care and segmentation of cardiac structures is required as a first …
2D-3D fully convolutional neural networks for cardiac MR segmentation
In this paper, we develop a 2D and 3D segmentation pipelines for fully automated cardiac
MR image segmentation using Deep Convolutional Neural Networks (CNN). Our models are …
MR image segmentation using Deep Convolutional Neural Networks (CNN). Our models are …
Automatic segmentation with detection of local segmentation failures in cardiac MRI
Segmentation of cardiac anatomical structures in cardiac magnetic resonance images
(CMRI) is a prerequisite for automatic diagnosis and prognosis of cardiovascular diseases …
(CMRI) is a prerequisite for automatic diagnosis and prognosis of cardiovascular diseases …
An exploration of 2D and 3D deep learning techniques for cardiac MR image segmentation
Accurate segmentation of the heart is an important step towards evaluating cardiac function.
In this paper, we present a fully automated framework for segmentation of the left (LV) and …
In this paper, we present a fully automated framework for segmentation of the left (LV) and …
A distance map regularized CNN for cardiac cine MR image segmentation
Purpose Cardiac image segmentation is a critical process for generating personalized
models of the heart and for quantifying cardiac performance parameters. Fully automatic …
models of the heart and for quantifying cardiac performance parameters. Fully automatic …
Densely connected fully convolutional network for short-axis cardiac cine MR image segmentation and heart diagnosis using random forest
In this paper, we propose a fully automatic method for segmentation of left ventricle, right
ventricle and myocardium from cardiac Magnetic Resonance (MR) images using densely …
ventricle and myocardium from cardiac Magnetic Resonance (MR) images using densely …
Multi-centre, multi-vendor and multi-disease cardiac segmentation: the M&Ms challenge
The emergence of deep learning has considerably advanced the state-of-the-art in cardiac
magnetic resonance (CMR) segmentation. Many techniques have been proposed over the …
magnetic resonance (CMR) segmentation. Many techniques have been proposed over the …
Recurrent fully convolutional neural networks for multi-slice MRI cardiac segmentation
In cardiac magnetic resonance imaging, fully-automatic segmentation of the heart enables
precise structural and functional measurements to be taken, eg from short-axis MR images …
precise structural and functional measurements to be taken, eg from short-axis MR images …
GridNet with automatic shape prior registration for automatic MRI cardiac segmentation
In this paper, we propose a fully automatic MRI cardiac segmentation method based on a
novel deep convolutional neural network (CNN) designed for the 2017 ACDC MICCAI …
novel deep convolutional neural network (CNN) designed for the 2017 ACDC MICCAI …