Adaptive preprocessing for generalization in cardiac MR image segmentation
Recent advances in deep learning have shown the capability to accurately segment cardiac
structures in magnetic resonance images. However, while these models provide a good …
structures in magnetic resonance images. However, while these models provide a good …
Adaptive preprocessing for generalization in cardiac MR image segmentation
C Haarburger - Statistical Atlases and Computational Models of …, 2021 - books.google.com
Recent advances in deep learning have shown the capability to accurately segment cardiac
structures in magnetic resonance images. However, while these models provide a good …
structures in magnetic resonance images. However, while these models provide a good …
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 …
A generalizable deep-learning approach for cardiac magnetic resonance image segmentation using image augmentation and attention U-Net
F Kong, SC Shadden - Statistical Atlases and Computational Models of the …, 2021 - Springer
Cardiac cine magnetic resonance imaging (CMRI) is the reference standard for assessing
cardiac structure as well as function. However, CMRI data presents large variations among …
cardiac structure as well as function. However, CMRI data presents large variations among …
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 …
A deep convolutional neural network approach for the segmentation of cardiac structures from MRI sequences
A Carscadden, M Noga, K Punithakumar - … Models of the Heart. M&Ms and …, 2021 - Springer
Cardiac magnetic resonance imaging typically generates hundreds of images in each scan,
and manual delineation of structures from these scans is tedious and time-consuming …
and manual delineation of structures from these scans is tedious and time-consuming …
Cardiac MRI left ventricular segmentation and function quantification using pre-trained neural networks
Deep learning has demonstrated promise for cardiac magnetic resonance image (MRI)
segmentation. However, the performance is degraded when a trained model is applied to …
segmentation. However, the performance is degraded when a trained model is applied to …
[HTML][HTML] Reducing segmentation failures in cardiac MRI via late feature fusion and GAN-based augmentation
Cardiac magnetic resonance (CMR) image segmentation is an integral step in the analysis
of cardiac function and diagnosis of heart related diseases. While recent deep learning …
of cardiac function and diagnosis of heart related diseases. While recent deep learning …
[HTML][HTML] Deep Learning Based Cardiac MRI Segmentation: Do We Need Experts?
Deep learning methods are the de facto solutions to a multitude of medical image analysis
tasks. Cardiac MRI segmentation is one such application, which, like many others, requires …
tasks. Cardiac MRI segmentation is one such application, which, like many others, requires …
[HTML][HTML] From accuracy to reliability and robustness in cardiac magnetic resonance image segmentation: a review
Since the rise of deep learning (DL) in the mid-2010s, cardiac magnetic resonance (CMR)
image segmentation has achieved state-of-the-art performance. Despite achieving inter …
image segmentation has achieved state-of-the-art performance. Despite achieving inter …