The red area is where subcutaneous fat is located. Auto: prediction made by deep learning; manual: the red line is drawn with ImageJ and the area outside the red line is used as input for subcutaneous fat.</p
Manual segmentation of muscle and adipose compartments from computed tomography (CT) axial images is...
Deep Learning approaches for automatic segmentation of organs from CT scans and MRI are providing pr...
Obesity is one of the main drivers of type 2 diabetes, but it is not uniformly associated with the d...
Prediction of the segmentation in the red area by deep learning is highly consistent with the input ...
Obesity is increasingly prevalent and associated with increased risk of developing type 2 diabetes, ...
<p>Subcutaneous adipose tissue is highlighted red, visceral adipose tissue is highlighted green, oth...
Large-scale studies, such as UK Biobank, acquire medical imaging data for thousands of participants....
Background: existing literature has highlighted structural, physiological, and pathological disparit...
Images were created using the Fiji package of ImageJ. All MR images were set to the dimensions of 35...
This research addresses the assessment of adipose tissue (AT) and spatial distribution of visceral (...
Obesity is one of the main drivers of type 2 diabetes, but it is not uniformly associated with the d...
We present several deep learning models for assessing the morphometric fidelity of deep grey matter ...
Fully automated and fast assessment of visceral and subcutaneous adipose tissue compartments using ...
Introduction: The present study aimed to suggest an unsupervised method for the segmentation of visc...
The purpose of the thesis was to investigate the possibility of using machine learn-ing for automati...
Manual segmentation of muscle and adipose compartments from computed tomography (CT) axial images is...
Deep Learning approaches for automatic segmentation of organs from CT scans and MRI are providing pr...
Obesity is one of the main drivers of type 2 diabetes, but it is not uniformly associated with the d...
Prediction of the segmentation in the red area by deep learning is highly consistent with the input ...
Obesity is increasingly prevalent and associated with increased risk of developing type 2 diabetes, ...
<p>Subcutaneous adipose tissue is highlighted red, visceral adipose tissue is highlighted green, oth...
Large-scale studies, such as UK Biobank, acquire medical imaging data for thousands of participants....
Background: existing literature has highlighted structural, physiological, and pathological disparit...
Images were created using the Fiji package of ImageJ. All MR images were set to the dimensions of 35...
This research addresses the assessment of adipose tissue (AT) and spatial distribution of visceral (...
Obesity is one of the main drivers of type 2 diabetes, but it is not uniformly associated with the d...
We present several deep learning models for assessing the morphometric fidelity of deep grey matter ...
Fully automated and fast assessment of visceral and subcutaneous adipose tissue compartments using ...
Introduction: The present study aimed to suggest an unsupervised method for the segmentation of visc...
The purpose of the thesis was to investigate the possibility of using machine learn-ing for automati...
Manual segmentation of muscle and adipose compartments from computed tomography (CT) axial images is...
Deep Learning approaches for automatic segmentation of organs from CT scans and MRI are providing pr...
Obesity is one of the main drivers of type 2 diabetes, but it is not uniformly associated with the d...