[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 …
[HTML][HTML] Federated learning for multi-center imaging diagnostics: a simulation study in cardiovascular disease
Deep learning models can enable accurate and efficient disease diagnosis, but have thus
far been hampered by the data scarcity present in the medical world. Automated diagnosis …
far been hampered by the data scarcity present in the medical world. Automated diagnosis …
CyCMIS: Cycle-consistent Cross-domain Medical Image Segmentation via diverse image augmentation
R Wang, G Zheng - Medical Image Analysis, 2022 - Elsevier
Abstract Domain shift, a phenomenon when there exists distribution discrepancy between
training dataset (source domain) and test dataset (target domain), is very common in …
training dataset (source domain) and test dataset (target domain), is very common in …
DeU-Net 2.0: Enhanced deformable U-Net for 3D cardiac cine MRI segmentation
Automatic segmentation of cardiac magnetic resonance imaging (MRI) facilitates efficient
and accurate volume measurement in clinical applications. However, due to anisotropic …
and accurate volume measurement in clinical applications. However, due to anisotropic …
Encoder modified U-net and feature pyramid network for multi-class segmentation of cardiac magnetic resonance images
Cardiovascular diseases are leading cause of death worldwide. Timely and accurate
detection of disease is required to reduce load on healthcare system and number of deaths …
detection of disease is required to reduce load on healthcare system and number of deaths …
[HTML][HTML] Evaluating tubulointerstitial compartments in renal biopsy specimens using a deep learning-based approach for classifying normal and abnormal tubules
Renal pathology is essential for diagnosing and assessing the severity and prognosis of
kidney diseases. Deep learning-based approaches have developed rapidly and have been …
kidney diseases. Deep learning-based approaches have developed rapidly and have been …
A Review of Automatic Cardiac Segmentation using Deep Learning and Deformable Models
B Rahmatikaregar, S Shirani… - … in Healthcare and …, 2022 - taylorfrancis.com
In this chapter, semi-or fully-automatic approaches for the segmentation of cardiovascular
images (especially approaches focused on the segmentation of the LV) that use deep …
images (especially approaches focused on the segmentation of the LV) that use deep …
DCNet: Diversity convolutional network for ventricle segmentation on short-axis cardiac magnetic resonance images
To accurately and simultaneously segment myocardium, left and right ventricles at the end-
diastolic (ED) and end-systolic (ES) phases from short-axis cardiac magnetic resonance …
diastolic (ED) and end-systolic (ES) phases from short-axis cardiac magnetic resonance …
Self-supervised assisted active learning for skin lesion segmentation
Label scarcity has been a long-standing issue for biomedical image segmentation, due to
high annotation costs and professional requirements. Recently, active learning (AL) …
high annotation costs and professional requirements. Recently, active learning (AL) …
A cascade approach for automatic segmentation of cardiac structures in short-axis cine-MR images using deep neural networks
Cardiovascular diseases are responsible for millions of deaths every year. In this scenario,
non-invasive exams such as cine-magnetic resonance imaging (cine-MRI) have favored a …
non-invasive exams such as cine-magnetic resonance imaging (cine-MRI) have favored a …