AIMC Topic: Tomography, X-Ray Computed

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Deep learning-based medical image segmentation with limited labels.

Physics in medicine and biology
Deep learning (DL)-based auto-segmentation has the potential for accurate organ delineation in radiotherapy applications but requires large amounts of clean labeled data to train a robust model. However, annotating medical images is extremely time-co...

The image quality of deep-learning image reconstruction of chest CT images on a mediastinal window setting.

Clinical radiology
AIM: To assess the image quality of deep-learning image reconstruction (DLIR) of chest computed tomography (CT) images on a mediastinal window setting in comparison to an adaptive statistical iterative reconstruction (ASiR-V).

Improving Image Quality and Reducing Radiation Dose for Pediatric CT by Using Deep Learning Reconstruction.

Radiology
Background CT deep learning reconstruction (DLR) algorithms have been developed to remove image noise. How the DLR affects image quality and radiation dose reduction has yet to be fully investigated. Purpose To investigate a DLR algorithm's dose redu...

An efficient mixture of deep and machine learning models for COVID-19 diagnosis in chest X-ray images.

PloS one
A newly emerged coronavirus (COVID-19) seriously threatens human life and health worldwide. In coping and fighting against COVID-19, the most critical step is to effectively screen and diagnose infected patients. Among them, chest X-ray imaging techn...

Machine Learning and Improved Quality Metrics in Acute Intracranial Hemorrhage by Noncontrast Computed Tomography.

Current problems in diagnostic radiology
OBJECTIVE: The timely reporting of critical results in radiology is paramount to improved patient outcomes. Artificial intelligence has the ability to improve quality by optimizing clinical radiology workflows. We sought to determine the impact of a ...

Separability of Acute Cerebral Infarction Lesions in CT Based Radiomics: Toward Artificial Intelligence-Assisted Diagnosis.

BioMed research international
This study aims at analyzing the separability of acute cerebral infarction lesions which were invisible in CT. 38 patients, who were diagnosed with acute cerebral infarction and performed both CT and MRI, and 18 patients, who had no positive finding ...

Using Auto-Segmentation to Reduce Contouring and Dose Inconsistency in Clinical Trials: The Simulated Impact on RTOG 0617.

International journal of radiation oncology, biology, physics
PURPOSE: Contouring inconsistencies are known but understudied in clinical radiation therapy trials. We applied auto-contouring to the Radiation Therapy Oncology Group (RTOG) 0617 dose escalation trial data. We hypothesized that the trial heart doses...

Automated size-specific dose estimates using deep learning image processing.

Medical image analysis
An automated vendor-independent system for dose monitoring in computed tomography (CT) medical examinations involving ionizing radiation is presented in this paper. The system provides precise size-specific dose estimates (SSDE) following the America...

Artificial Intelligence for Optimization and Interpretation of PET/CT and PET/MR Images.

Seminars in nuclear medicine
Artificial intelligence (AI) has recently attracted much attention for its potential use in healthcare applications. The use of AI to improve and extract more information out of medical images, given their parallels with natural images and the immens...

COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images.

Scientific reports
The Coronavirus Disease 2019 (COVID-19) pandemic continues to have a devastating effect on the health and well-being of the global population. A critical step in the fight against COVID-19 is effective screening of infected patients, with one of the ...