Deep learning in mesoscale brain microscopy image analysis: A review
R Chen, M Liu, W Chen, Y Wang, E Meijering - Computers in Biology and …, 2023 - Elsevier
Mesoscale microscopy images of the brain contain a wealth of information which can help
us understand the working mechanisms of the brain. However, it is a challenging task to …
us understand the working mechanisms of the brain. However, it is a challenging task to …
Unified medical image segmentation by learning from uncertainty in an end-to-end manner
Automatic segmentation is a fundamental task in computer-assisted medical image analysis.
Convolutional neural networks (CNNs) have been widely used for medical image …
Convolutional neural networks (CNNs) have been widely used for medical image …
[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 …
FD-Net: Feature distillation network for oral squamous cell carcinoma lymph node segmentation in hyperspectral imagery
Oral squamous cell carcinoma (OSCC) has the characteristics of early regional lymph node
metastasis. OSCC patients often have poor prognoses and low survival rates due to cervical …
metastasis. OSCC patients often have poor prognoses and low survival rates due to cervical …
Cross-convolutional transformer for automated multi-organs segmentation in a variety of medical images
J Wang, H Zhao, W Liang, S Wang… - Physics in Medicine & …, 2023 - iopscience.iop.org
Objective. It is a huge challenge for multi-organs segmentation in various medical images
based on a consistent algorithm with the development of deep learning methods. We …
based on a consistent algorithm with the development of deep learning methods. We …
Papillary-muscle-derived radiomic features for hypertrophic cardiomyopathy versus hypertensive heart disease classification
Q Liu, Q Lu, Y Chai, Z Tao, Q Wu, M Jiang, J Pu - Diagnostics, 2023 - mdpi.com
Purpose: This study aimed to assess the value of radiomic features derived from the
myocardium (MYO) and papillary muscle (PM) for left ventricular hypertrophy (LVH) …
myocardium (MYO) and papillary muscle (PM) for left ventricular hypertrophy (LVH) …
Levy flight and chaos theory-based gravitational search algorithm for image segmentation
Image segmentation is one of the pivotal steps in image processing due to its enormous
application potential in medical image analysis, data mining, and pattern recognition. In fact …
application potential in medical image analysis, data mining, and pattern recognition. In fact …
Left ventricular trabeculations at cardiac MRI: reference ranges and association with cardiovascular risk factors in UK Biobank
Background The extent of left ventricular (LV) trabeculation and its relationship with
cardiovascular (CV) risk factors is unclear. Purpose To apply automated segmentation to UK …
cardiovascular (CV) risk factors is unclear. Purpose To apply automated segmentation to UK …
Conformal performance range prediction for segmentation output quality control
Recent works have introduced methods to estimate segmentation performance without
ground truth, relying solely on neural network softmax outputs. These techniques hold …
ground truth, relying solely on neural network softmax outputs. These techniques hold …
Autopaint: A self-inpainting method for unsupervised anomaly detection
Robust and accurate detection and segmentation of heterogenous tumors appearing in
different anatomical organs with supervised methods require large-scale labeled datasets …
different anatomical organs with supervised methods require large-scale labeled datasets …