AIMC Topic: Tomography, X-Ray Computed

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A comprehensive study on classification of COVID-19 on computed tomography with pretrained convolutional neural networks.

Scientific reports
The use of imaging data has been reported to be useful for rapid diagnosis of COVID-19. Although computed tomography (CT) scans show a variety of signs caused by the viral infection, given a large amount of images, these visual features are difficult...

Robust deep learning classification of adamantinomatous craniopharyngioma from limited preoperative radiographic images.

Scientific reports
Deep learning (DL) is a widely applied mathematical modeling technique. Classically, DL models utilize large volumes of training data, which are not available in many healthcare contexts. For patients with brain tumors, non-invasive diagnosis would r...

Development and evaluation of an artificial intelligence system for COVID-19 diagnosis.

Nature communications
Early detection of COVID-19 based on chest CT enables timely treatment of patients and helps control the spread of the disease. We proposed an artificial intelligence (AI) system for rapid COVID-19 detection and performed extensive statistical analys...

Noise reduction approach in pediatric abdominal CT combining deep learning and dual-energy technique.

European radiology
OBJECTIVES: To evaluate the image quality of low iodine concentration, dual-energy CT (DECT) combined with a deep learning-based noise reduction technique for pediatric abdominal CT, compared with standard iodine concentration single-energy polychrom...

Dual-branch combination network (DCN): Towards accurate diagnosis and lesion segmentation of COVID-19 using CT images.

Medical image analysis
The recent global outbreak and spread of coronavirus disease (COVID-19) makes it an imperative to develop accurate and efficient diagnostic tools for the disease as medical resources are getting increasingly constrained. Artificial intelligence (AI)-...

Complete abdomen and pelvis segmentation using U-net variant architecture.

Medical physics
PURPOSE: Organ segmentation of computed tomography (CT) imaging is essential for radiotherapy treatment planning. Treatment planning requires segmentation not only of the affected tissue, but nearby healthy organs-at-risk, which is laborious and time...

Self-derived organ attention for unpaired CT-MRI deep domain adaptation based MRI segmentation.

Physics in medicine and biology
To develop and evaluate a deep learning method to segment parotid glands from MRI using unannotated MRI and unpaired expert-segmented CT datasets. We introduced a new self-derived organ attention deep learning network for combined CT to MRI image-to-...

Technical Note: Automatic segmentation of CT images for ventral body composition analysis.

Medical physics
PURPOSE: Body composition is known to be associated with many diseases including diabetes, cancers, and cardiovascular diseases. In this paper, we developed a fully automatic body tissue decomposition procedure to segment three major compartments tha...

Use of artificial intelligence to recover mandibular morphology after disease.

Scientific reports
Mandibular tumors and radical oral cancer surgery often cause bone dysmorphia and defects. Most patients present with noticeable mandibular deformations, and doctors often have difficulty determining their exact mandibular morphology. In this study, ...