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View article: Shape Deformation Networks for Automated Aortic Valve Finite Element Meshing from 3D CT Images
Shape Deformation Networks for Automated Aortic Valve Finite Element Meshing from 3D CT Images Open
Accurate geometric modeling of the aortic valve from 3D CT images is essential for biomechanical analysis and patient-specific simulations to assess valve health or make a preoperative plan. However, it remains challenging to generate aort…
View article: FEAorta: A Fully Automated Framework for Finite Element Analysis of the Aorta From 3D CT Images
FEAorta: A Fully Automated Framework for Finite Element Analysis of the Aorta From 3D CT Images Open
Aortic aneurysm disease ranks consistently in the top 20 causes of death in the U.S. population. Thoracic aortic aneurysm is manifested as an abnormal bulging of thoracic aortic wall and it is a leading cause of death in adults. From the p…
View article: A Computational Pipeline for Patient-Specific Modeling of Thoracic Aortic Aneurysm: From Medical Image to Finite Element Analysis
A Computational Pipeline for Patient-Specific Modeling of Thoracic Aortic Aneurysm: From Medical Image to Finite Element Analysis Open
The aorta is the body's largest arterial vessel, serving as the primary pathway for oxygenated blood within the systemic circulation. Aortic aneurysms consistently rank among the top twenty causes of mortality in the United States. Thoraci…
View article: A 3D Image Segmentation Study of U-Net on CT Images of the Human Aorta with Morphologically Diverse Anatomy
A 3D Image Segmentation Study of U-Net on CT Images of the Human Aorta with Morphologically Diverse Anatomy Open
For the machine learning -assisted diagnosis of cardiac diseases, such as thoracic aortic aneurysm, the geometries of the heart and blood vessels need to be reconstructed from medical images, which is usually done by image segmentation fol…
View article: Evaluation of Deep Neural Network Models for Instance Segmentation of Lumbar Spine MRI
Evaluation of Deep Neural Network Models for Instance Segmentation of Lumbar Spine MRI Open
Intervertebral disc disease, a prevalent ailment, frequently leads to intermittent or persistent low back pain, and diagnosing and assessing of this disease rely on accurate measurement of vertebral bone and intervertebral disc geometries …
View article: A general approach to improve adversarial robustness of DNNs for medical image segmentation and detection
A general approach to improve adversarial robustness of DNNs for medical image segmentation and detection Open
It is known that deep neural networks (DNNs) are vulnerable to adversarial noises. Improving adversarial robustness of DNNs is essential. This is not only because unperceivable adversarial noise is a threat to the performance of DNNs model…
View article: Attention-based Shape-Deformation Networks for Artifact-Free Geometry Reconstruction of Lumbar Spine from MR Images
Attention-based Shape-Deformation Networks for Artifact-Free Geometry Reconstruction of Lumbar Spine from MR Images Open
Lumbar disc degeneration, a progressive structural wear and tear of lumbar intervertebral disc, is regarded as an essential role on low back pain, a significant global health concern. Automated lumbar spine geometry reconstruction from MR …
View article: SymTC: A Symbiotic Transformer-CNN Net for Instance Segmentation of Lumbar Spine MRI
SymTC: A Symbiotic Transformer-CNN Net for Instance Segmentation of Lumbar Spine MRI Open
Intervertebral disc disease, a prevalent ailment, frequently leads to intermittent or persistent low back pain, and diagnosing and assessing of this disease rely on accurate measurement of vertebral bone and intervertebral disc geometries …
View article: Adversarial Robustness Study of Convolutional Neural Network for Lumbar Disk Shape Reconstruction from MR images
Adversarial Robustness Study of Convolutional Neural Network for Lumbar Disk Shape Reconstruction from MR images Open
Machine learning technologies using deep neural networks (DNNs), especially convolutional neural networks (CNNs), have made automated, accurate, and fast medical image analysis a reality for many applications, and some DNN-based medical im…
View article: CQ-VAE: Coordinate Quantized VAE for Uncertainty Estimation with Application to Disk Shape Analysis from Lumbar Spine MRI Images
CQ-VAE: Coordinate Quantized VAE for Uncertainty Estimation with Application to Disk Shape Analysis from Lumbar Spine MRI Images Open
Ambiguity is inevitable in medical images, which often results in different image interpretations (e.g. object boundaries or segmentation maps) from different human experts. Thus, a model that learns the ambiguity and outputs a probability…
View article: CQ-VAE: Coordinate Quantized VAE for Uncertainty Estimation with\n Application to Disk Shape Analysis from Lumbar Spine MRI Images
CQ-VAE: Coordinate Quantized VAE for Uncertainty Estimation with\n Application to Disk Shape Analysis from Lumbar Spine MRI Images Open
Ambiguity is inevitable in medical images, which often results in different\nimage interpretations (e.g. object boundaries or segmentation maps) from\ndifferent human experts. Thus, a model that learns the ambiguity and outputs a\nprobabil…
View article: An Algorithm for Out-Of-Distribution Attack to Neural Network Encoder
An Algorithm for Out-Of-Distribution Attack to Neural Network Encoder Open
Deep neural networks (DNNs), especially convolutional neural networks, have achieved superior performance on image classification tasks. However, such performance is only guaranteed if the input to a trained model is similar to the trainin…
View article: An Algorithm to Attack Neural Network Encoder-based Out-Of-Distribution Sample Detector
An Algorithm to Attack Neural Network Encoder-based Out-Of-Distribution Sample Detector Open
Deep neural network (DNN), especially convolutional neural network, has achieved superior performance on image classification tasks. However, such performance is only guaranteed if the input to a trained model is similar to the training sa…