AIMC Topic: Radiographic Image Interpretation, Computer-Assisted

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Convolutional Neural Networks for Segmentation of Pleural Mesothelioma: Analysis of Probability Map Thresholds (CALGB 30901, Alliance).

Journal of imaging informatics in medicine
The purpose of this study was to evaluate the impact of probability map threshold on pleural mesothelioma (PM) tumor delineations generated using a convolutional neural network (CNN). One hundred eighty-six CT scans from 48 PM patients were segmented...

Skeleton-guided 3D convolutional neural network for tubular structure segmentation.

International journal of computer assisted radiology and surgery
PURPOSE: Accurate segmentation of tubular structures is crucial for clinical diagnosis and treatment but is challenging due to their complex branching structures and volume imbalance. The purpose of this study is to propose a 3D deep learning network...

Advancements in supervised deep learning for metal artifact reduction in computed tomography: A systematic review.

European journal of radiology
BACKGROUND: Metallic artefacts caused by metal implants, are a common problem in computed tomography (CT) imaging, degrading image quality and diagnostic accuracy. With advancements in artificial intelligence, novel deep learning (DL)-based metal art...

Low-dose computed tomography perceptual image quality assessment.

Medical image analysis
In computed tomography (CT) imaging, optimizing the balance between radiation dose and image quality is crucial due to the potentially harmful effects of radiation on patients. Although subjective assessments by radiologists are considered the gold s...

Hematoma expansion prediction in intracerebral hemorrhage patients by using synthesized CT images in an end-to-end deep learning framework.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Spontaneous intracerebral hemorrhage (ICH) is a type of stroke less prevalent than ischemic stroke but associated with high mortality rates. Hematoma expansion (HE) is an increase in the bleeding that affects 30%-38% of hemorrhagic stroke patients. I...

Reconstruction of patient-specific confounders in AI-based radiologic image interpretation using generative pretraining.

Cell reports. Medicine
Reliably detecting potentially misleading patterns in automated diagnostic assistance systems, such as those powered by artificial intelligence (AI), is crucial for instilling user trust and ensuring reliability. Current techniques fall short in visu...

Combining Multistaged Filters and Modified Segmentation Network for Improving Lung Nodules Classification.

IEEE journal of biomedical and health informatics
Advancements in computational technology have led to a shift towards automated detection processes in lung cancer screening, particularly through nodule segmentation techniques. These techniques employ thresholding to distinguish between soft and fir...

Artificial intelligence assisted automatic screening of opportunistic osteoporosis in computed tomography images from different scanners.

European radiology
OBJECTIVES: It is feasible to evaluate bone mineral density (BMD) and detect osteoporosis through an artificial intelligence (AI)-assisted system by using quantitative computed tomography (QCT) as a reference without additional radiation exposure or ...

Volume Measurements for Surveillance after Endovascular Aneurysm Repair using Artificial Intelligence.

European journal of vascular and endovascular surgery : the official journal of the European Society for Vascular Surgery
OBJECTIVE: Surveillance after endovascular aneurysm repair (EVAR) is suboptimal due to limited compliance and relatively large variability in measurement methods of abdominal aortic aneurysm (AAA) sac size after treatment. Measuring volume offers a m...