AIMC Topic: Tomography, X-Ray Computed

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Opportunistic osteoporosis screening in multi-detector CT images using deep convolutional neural networks.

European radiology
OBJECTIVE: To explore the application of deep learning in patients with primary osteoporosis, and to develop a fully automatic method based on deep convolutional neural network (DCNN) for vertebral body segmentation and bone mineral density (BMD) cal...

Accurate and Efficient Intracranial Hemorrhage Detection and Subtype Classification in 3D CT Scans with Convolutional and Long Short-Term Memory Neural Networks.

Sensors (Basel, Switzerland)
In this paper, we present our system for the RSNA Intracranial Hemorrhage Detection challenge, which is based on the RSNA 2019 Brain CT Hemorrhage dataset. The proposed system is based on a lightweight deep neural network architecture composed of a c...

Potential use of deep learning techniques for postmortem imaging.

Forensic science, medicine, and pathology
The use of postmortem computed tomography in forensic medicine, in addition to conventional autopsy, is now a standard procedure in several countries. However, the large number of cases, the large amount of data, and the lack of postmortem radiology ...

Machine Learning-Based Differentiation of Nontuberculous Mycobacteria Lung Disease and Pulmonary Tuberculosis Using CT Images.

BioMed research international
An increasing number of patients infected with nontuberculous mycobacteria (NTM) are observed worldwide. However, it is challenging to identify NTM lung diseases from pulmonary tuberculosis (PTB) due to considerable overlap in classic manifestations ...

Clinical evaluation of atlas- and deep learning-based automatic segmentation of multiple organs and clinical target volumes for breast cancer.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Manual segmentation is the gold standard method for radiation therapy planning; however, it is time-consuming and prone to inter- and intra-observer variation, giving rise to interests in auto-segmentation methods. We evaluated the feasibility of dee...

Multi-view radiomics and dosiomics analysis with machine learning for predicting acute-phase weight loss in lung cancer patients treated with radiotherapy.

Physics in medicine and biology
We propose a multi-view data analysis approach using radiomics and dosiomics (R&D) texture features for predicting acute-phase weight loss (WL) in lung cancer radiotherapy. Baseline weight of 388 patients who underwent intensity modulated radiation t...

3D Deep Neural Network Segmentation of Intracerebral Hemorrhage: Development and Validation for Clinical Trials.

Neuroinformatics
Intracranial hemorrhage (ICH) occurs when a blood vessel ruptures in the brain. This leads to significant morbidity and mortality, the likelihood of which is predicated on the size of the bleeding event. X-ray computed tomography (CT) scans allow cli...

Truncation compensation and metallic dental implant artefact reduction in PET/MRI attenuation correction using deep learning-based object completion.

Physics in medicine and biology
The susceptibility of MRI to metallic objects leads to void MR signal and missing information around metallic implants. In addition, body truncation occurs in MR imaging for large patients who exceed the transaxial field-of-view of the scanner. Body ...

The use of artificial intelligence in computed tomography image reconstruction - A literature review.

Journal of medical imaging and radiation sciences
BACKGROUND AND PURPOSE: The use of AI in the process of CT image reconstruction may improve image quality of resultant images and therefore facilitate low-dose CT examinations.