AIMC Topic: Tomography, X-Ray Computed

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Exemplar deep and hand-modeled features based automated and accurate cerebral hemorrhage classification method.

Medical engineering & physics
BACKGROUND: Cerebral hemorrhage (CH) is a commonly seen disease, and an accurate diagnosis of the type of CH is a very crucial step in treatment. Therefore, CH requires a prompt and accurate diagnosis. To simplify this process, an accurate CH classif...

Development and validation of an ensemble artificial intelligence model for comprehensive imaging quality check to classify body parts and contrast enhancement.

BMC medical imaging
BACKGROUND: Despite the dramatic increase in the use of medical imaging in various therapeutic fields of clinical trials, the first step of image quality check (image QC), which aims to check whether images are uploaded appropriately according to the...

A geometry-guided multi-beamlet deep learning technique for CT reconstruction.

Biomedical physics & engineering express
. Previous studies have proposed deep-learning techniques to reconstruct CT images from sinograms. However, these techniques employ large fully-connected (FC) layers for projection-to-image domain transformation, producing large models requiring subs...

A lightweight CNN-based network on COVID-19 detection using X-ray and CT images.

Computers in biology and medicine
BACKGROUND AND OBJECTIVES: The traditional method of detecting COVID-19 disease mainly rely on the interpretation of computer tomography (CT) or X-ray images (X-ray) by doctors or professional researchers to identify whether it is COVID-19 disease, w...

Mutual enhancing learning-based automatic segmentation of CT cardiac substructure.

Physics in medicine and biology
Current segmentation practice for thoracic cancer RT considers the whole heart as a single organ despite increased risks of cardiac toxicities from irradiation of specific cardiac substructures. Segmenting up to 15 different cardiac substructures can...

A deep learning-based precision volume calculation approach for kidney and tumor segmentation on computed tomography images.

Computer methods and programs in biomedicine
Previously, doctors interpreted computed tomography (CT) images based on their experience in diagnosing kidney diseases. However, with the rapid increase in CT images, such interpretations were required considerable time and effort, producing inconsi...

Early severity prediction of BPD for premature infants from chest X-ray images using deep learning: A study at the 28th day of oxygen inhalation.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Bronchopulmonary dysplasia is a common respiratory disease in premature infants. The severity is diagnosed at the 56th day after birth or discharge by analyzing the clinical indicators, which may cause the delay of the best ...

A deep learning-based approach for the diagnosis of adrenal adenoma: a new trial using CT.

The British journal of radiology
OBJECTIVE: To develop and validate deep convolutional neural network (DCNN) models for the diagnosis of adrenal adenoma (AA) using CT.

Deep Learning-Based Computed Tomography Perfusion Imaging to Evaluate the Effectiveness and Safety of Thrombolytic Therapy for Cerebral Infarct with Unknown Time of Onset.

Contrast media & molecular imaging
This study was aimed to discuss the effectiveness and safety of deep learning-based computed tomography perfusion (CTP) imaging in the thrombolytic therapy for acute cerebral infarct with unknown time of onset. A total of 100 patients with acute cere...

Early Diagnosis of Acute Ischemic Stroke by Brain Computed Tomography Perfusion Imaging Combined with Head and Neck Computed Tomography Angiography on Deep Learning Algorithm.

Contrast media & molecular imaging
The purpose of the research was to discuss the application values of deep learning algorithm-based computed tomography perfusion (CTP) imaging combined with head and neck computed tomography angiography (CTA) in the diagnosis of ultra-early acute isc...