Medical image analysis plays an important role in computer-aided liver-carcinoma diagnosis. Aiming at the existing image fuzzy clustering segmentation being not suitable to segment CT image with non-uniform background, a fast robust kernel space fuzz...
Utilizing the idea of long-term cumulative return, reinforcement learning (RL) has shown remarkable performance in various fields. We follow the formulation of landmark localization in 3D medical images as an RL problem. Whereas value-based methods h...
The use of Computed Tomography (CT) imaging for patients with stroke symptoms is an essential step for triaging and diagnosis in many hospitals. However, the subtle expression of ischemia in acute CT images has made it hard for automated methods to e...
Dual-energy computed tomography (DECT) imaging plays an important role in advanced imaging applications due to its material decomposition capability. Image-domain decomposition operates directly on CT images using linear matrix inversion, but the dec...
PURPOSE: To study the influence of gradient echo-based contrasts as input channels to a 3D patch-based neural network trained for synthetic CT (sCT) generation in canine and human populations.
Computer methods and programs in biomedicine
Oct 8, 2019
BACKGROUND AND OBJECTIVE: X-ray computed tomography (CT) plays an important role in modern medical science. Human health problems caused by CT radiation have attracted the attention of the academic community widely. Reducing radiation dose results in...
Computer methods and programs in biomedicine
Oct 4, 2019
BACKGROUND AND OBJECTIVE: We investigated a novel method using a 2D convolutional neural network (CNN) to identify superior and inferior vertebrae in a single slice of CT images, and a post-processing for 3D segmentation and separation of cervical ve...
The finite element method (FEM) is among the most commonly used numerical methods for solving engineering problems. Due to its computational cost, various ideas have been introduced to reduce computation times, such as domain decomposition, parallel ...
AIM: To realise the automated prediction of soft-tissue sarcoma (STS) grading and lung metastasis based on computed tomography (CT), T1-weighted (T1W) magnetic resonance imaging (MRI), and fat-suppressed T2-weighted MRI (FST2W) via the convolutional ...
PURPOSE: Prompt diagnosis and quantitation of pneumothorax impact decisions pertaining to patient management. The purpose of our study was to develop and evaluate the accuracy of a deep learning (DL)-based image classification program for detection o...
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