Medical Image Segmentation based on U-Net: A Review.

G Du, X Cao, J Liang, X Chen… - Journal of Imaging …, 2020 - search.ebscohost.com
Medical image analysis is performed by analyzing images obtained by medical imaging
systems to solve clinical problems. The purpose is to extract effective information and …

Deep learning-based automatic segmentation of images in cardiac radiography: a promising challenge

Y Song, S Ren, Y Lu, X Fu, KKL Wong - Computer Methods and Programs …, 2022 - Elsevier
Background Due to the advancement of medical imaging and computer technology,
machine intelligence to analyze clinical image data increases the probability of disease …

Saunet: Shape attentive u-net for interpretable medical image segmentation

J Sun, F Darbehani, M Zaidi, B Wang - … , Lima, Peru, October 4–8, 2020 …, 2020 - Springer
Medical image segmentation is a difficult but important task for many clinical operations such
as cardiac bi-ventricular volume estimation. More recently, there has been a shift to utilizing …

Deep learning in cardiology

P Bizopoulos, D Koutsouris - IEEE reviews in biomedical …, 2018 - ieeexplore.ieee.org
The medical field is creating large amount of data that physicians are unable to decipher
and use efficiently. Moreover, rule-based expert systems are inefficient in solving …

Automated diagnosis of cardiovascular diseases from cardiac magnetic resonance imaging using deep learning models: A review

M Jafari, A Shoeibi, M Khodatars, N Ghassemi… - Computers in Biology …, 2023 - Elsevier
In recent years, cardiovascular diseases (CVDs) have become one of the leading causes of
mortality globally. At early stages, CVDs appear with minor symptoms and progressively get …

[HTML][HTML] Automated analysis of cardiovascular magnetic resonance myocardial native T1 mapping images using fully convolutional neural networks

AS Fahmy, H El-Rewaidy, M Nezafat… - Journal of …, 2019 - Elsevier
Background Cardiovascular magnetic resonance (CMR) myocardial native T 1 mapping
allows assessment of interstitial diffuse fibrosis. In this technique, the global and regional T 1 …

Semi-supervised generative adversarial networks for the segmentation of the left ventricle in pediatric MRI

C Decourt, L Duong - Computers in Biology and Medicine, 2020 - Elsevier
Segmentation of the left ventricle in magnetic resonance imaging (MRI) is important for
assessing cardiac function. We present DT-GAN, a generative adversarial network (GAN) …

Automatic segmentation of brain tumour in MR images using an enhanced deep learning approach

S Tripathi, A Verma, N Sharma - Computer Methods in …, 2021 - Taylor & Francis
The presented manuscript proposes a fully automatic deep learning method to quantify the
tumour region in brain Magnetic Resonance images as the accurate diagnosis of brain …

Left ventricle segmentation in cardiac MR: A systematic mapping of the past decade

MAO Ribeiro, FLS Nunes - ACM Computing Surveys (CSUR), 2022 - dl.acm.org
Left ventricle segmentation in short-axis cardiac magnetic resonance images is important to
diagnose heart disease. However, repetitive manual segmentation of these images requires …

[HTML][HTML] Left ventricle segmentation combining deep learning and deformable models with anatomical constraints

MAO Ribeiro, FLS Nunes - Journal of Biomedical Informatics, 2023 - Elsevier
Segmentation of the left ventricle is a key approach in Cardiac Magnetic Resonance
Imaging for calculating biomarkers in diagnosis. Since there is substantial effort required …