AIMC Topic: Tomography, X-Ray Computed

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Artificial intelligence-driven assessment of radiological images for COVID-19.

Computers in biology and medicine
Artificial Intelligence (AI) methods have significant potential for diagnosis and prognosis of COVID-19 infections. Rapid identification of COVID-19 and its severity in individual patients is expected to enable better control of the disease individua...

Value of Rehabilitation Training for Children with Cerebral Palsy Diagnosed and Analyzed by Computed Tomography Imaging Information Features under Deep Learning.

Journal of healthcare engineering
To analyze the brain CT imaging data of children with cerebral palsy (CP), deep learning-based electronic computed tomography (CT) imaging information characteristics were used, thereby providing help for the rehabilitation analysis of children with ...

CT Image Analysis and Clinical Diagnosis of New Coronary Pneumonia Based on Improved Convolutional Neural Network.

Computational and mathematical methods in medicine
In this paper, based on the improved convolutional neural network, in-depth analysis of the CT image of the new coronary pneumonia, using the U-Net series of deep neural networks to semantically segment the CT image of the new coronary pneumonia, to ...

Pixel-wise body composition prediction with a multi-task conditional generative adversarial network.

Journal of biomedical informatics
The analysis of human body composition plays a critical role in health management and disease prevention. However, current medical technologies to accurately assess body composition such as dual energy X-ray absorptiometry, computed tomography, and m...

Recognition of honeycomb lung in CT images based on improved MobileNet model.

Medical physics
PURPOSE: The research is to improve the efficiency and accuracy of recognition of honeycomb lung in CT images.

Contrast-enhanced to noncontrast CT transformation via an adjacency content-transfer-based deep subtraction residual neural network.

Physics in medicine and biology
To reduce overall patient radiation exposure in some clinical scenarios (since cancer patients need frequent follow-ups), noncontrast CT is not used in some institutions. However, although less desirable, noncontrast CT could provide additional impor...

Comparing different CT, PET and MRI multi-modality image combinations for deep learning-based head and neck tumor segmentation.

Acta oncologica (Stockholm, Sweden)
BACKGROUND: Manual delineation of gross tumor volume (GTV) is essential for radiotherapy treatment planning, but it is time-consuming and suffers inter-observer variability (IOV). In clinics, CT, PET, and MRI are used to inform delineation accuracy d...

Three-stage segmentation of lung region from CT images using deep neural networks.

BMC medical imaging
BACKGROUND: Lung region segmentation is an important stage of automated image-based approaches for the diagnosis of respiratory diseases. Manual methods executed by experts are considered the gold standard, but it is time consuming and the accuracy i...