Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation
The medical imaging literature has witnessed remarkable progress in high-performing
segmentation models based on convolutional neural networks. Despite the new …
segmentation models based on convolutional neural networks. Despite the new …
[HTML][HTML] Deep learning in spatiotemporal cardiac imaging: A review of methodologies and clinical usability
KAL Hernandez, T Rienmüller, D Baumgartner… - Computers in Biology …, 2021 - Elsevier
The use of different cardiac imaging modalities such as MRI, CT or ultrasound enables the
visualization and interpretation of altered morphological structures and function of the heart …
visualization and interpretation of altered morphological structures and function of the heart …
Multi-level semantic adaptation for few-shot segmentation on cardiac image sequences
Obtaining manual labels is time-consuming and labor-intensive on cardiac image
sequences. Few-shot segmentation can utilize limited labels to learn new tasks. However, it …
sequences. Few-shot segmentation can utilize limited labels to learn new tasks. However, it …
Semi-supervised segmentation of echocardiography videos via noise-resilient spatiotemporal semantic calibration and fusion
H Wu, J Liu, F Xiao, Z Wen, L Cheng, J Qin - Medical Image Analysis, 2022 - Elsevier
We present a novel model for left ventricle endocardium segmentation from
echocardiography video, which is of great significance in clinical practice and yet a …
echocardiography video, which is of great significance in clinical practice and yet a …
MV-RAN: Multiview recurrent aggregation network for echocardiographic sequences segmentation and full cardiac cycle analysis
Multiview based learning has generally returned dividends in performance because
additional information can be extracted for the representation of the diversity of different …
additional information can be extracted for the representation of the diversity of different …
Temporal-consistent segmentation of echocardiography with co-learning from appearance and shape
Accurate and temporal-consistent segmentation of echocardiography is important for
diagnosing cardiovascular disease. Existing methods often ignore consistency among the …
diagnosing cardiovascular disease. Existing methods often ignore consistency among the …
Unsupervised inter-frame motion correction for whole-body dynamic PET using convolutional long short-term memory in a convolutional neural network
Subject motion in whole-body dynamic PET introduces inter-frame mismatch and seriously
impacts parametric imaging. Traditional non-rigid registration methods are generally …
impacts parametric imaging. Traditional non-rigid registration methods are generally …
Multi-task learning with multi-view weighted fusion attention for artery-specific calcification analysis
W Zhang, G Yang, N Zhang, L Xu, X Wang, Y Zhang… - Information …, 2021 - Elsevier
In general, artery-specific calcification analysis comprises the simultaneous calcification
segmentation and quantification tasks. It can help provide a thorough assessment for …
segmentation and quantification tasks. It can help provide a thorough assessment for …
Co-learning of appearance and shape for precise ejection fraction estimation from echocardiographic sequences
Accurate estimation of ejection fraction (EF) from echocardiography is of great importance
for evaluation of cardiac function. It is usually obtained by the Simpson's bi-plane method …
for evaluation of cardiac function. It is usually obtained by the Simpson's bi-plane method …
Improved segmentation of echocardiography with orientation-congruency of optical flow and motion-enhanced segmentation
Quantification of left ventricular (LV) ejection fraction (EF) from echocardiography depends
upon the identification of endocardium boundaries as well as the calculation of end-diastolic …
upon the identification of endocardium boundaries as well as the calculation of end-diastolic …