AIMC Topic: Tomography, X-Ray Computed

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Deep-learning-based hepatic fat assessment (DeHFt) on non-contrast chest CT and its association with disease severity in COVID-19 infections: A multi-site retrospective study.

EBioMedicine
BACKGROUND: Hepatic steatosis (HS) identified on CT may provide an integrated cardiometabolic and COVID-19 risk assessment. This study presents a deep-learning-based hepatic fat assessment (DeHFt) pipeline for (a) more standardised measurements and (...

Deep Learning Segmentation and Reconstruction for CT of Chronic Total Coronary Occlusion.

Radiology
Background CT imaging of chronic total occlusion (CTO) is useful in guiding revascularization, but manual reconstruction and quantification are time consuming. Purpose To develop and validate a deep learning (DL) model for automated CTO reconstructio...

Strong semantic segmentation for Covid-19 detection: Evaluating the use of deep learning models as a performant tool in radiography.

Radiography (London, England : 1995)
INTRODUCTION: With the increasing number of Covid-19 cases as well as care costs, chest diseases have gained increasing interest in several communities, particularly in medical and computer vision. Clinical and analytical exams are widely recognized ...

Kidney Tumor Detection and Classification Based on Deep Learning Approaches: A New Dataset in CT Scans.

Journal of healthcare engineering
Kidney tumor (KT) is one of the diseases that have affected our society and is the seventh most common tumor in both men and women worldwide. The early detection of KT has significant benefits in reducing death rates, producing preventive measures th...

Clinical target volume segmentation based on gross tumor volume using deep learning for head and neck cancer treatment.

Medical dosimetry : official journal of the American Association of Medical Dosimetrists
Accurate clinical target volume (CTV) delineation is important for head and neck intensity-modulated radiation therapy. However, delineation is time-consuming and susceptible to interobserver variability (IOV). Based on a manual contouring process co...

Virtual and real-world implementation of deep-learning-based image denoising model on projection domain in digital tomosynthesis and cone-beam computed tomography data.

Biomedical physics & engineering express
Reducing the radiation dose will cause severe image noise and artifacts, and degradation of image quality will also affect the accuracy of diagnosis. To find a solution, we comprise a 2D and 3D concatenating convolutional encoder-decoder (CCE-3D) and...

Abdominopelvic CT Image Quality: Evaluation of Thin (0.5-mm) Slices Using Deep Learning Reconstruction.

AJR. American journal of roentgenology
Because thick-section images (typically 3-5 mm) have low image noise, radiologists typically use them to perform clinical interpretation, although they may additionally refer to thin-section images (typically 0.5-0.625 mm) for problem solving. Deep ...

A high-performance method of deep learning for prostate MR-only radiotherapy planning using an optimized Pix2Pix architecture.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: The first aim was to generate and compare synthetic-CT (sCT) images using a conditional generative adversarial network (cGAN) method (Pix2Pix) for MRI-only prostate radiotherapy planning by testing several generators, loss functions, and hyp...