AIMC Topic: Tomography, X-Ray Computed

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Verte-Box: A Novel Convolutional Neural Network for Fully Automatic Segmentation of Vertebrae in CT Image.

Tomography (Ann Arbor, Mich.)
Due to the complex shape of the vertebrae and the background containing a lot of interference information, it is difficult to accurately segment the vertebrae from the computed tomography (CT) volume by manual segmentation. This paper proposes a conv...

Using Deep Learning to Automate the Detection of Flaws in Nuclear Fuel Channel UT Scans.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Nuclear reactor inspections are critical to ensure the safety and reliability of a nuclear facility's operation. In Canada, ultrasonic testing (UT) is used to inspect the health of pressure tubes that are part of Canada's Deuterium Uranium (CANDU) re...

Optical coherence tomography for identification of malignant pulmonary nodules based on random forest machine learning algorithm.

PloS one
OBJECTIVE: To explore the feasibility of using random forest (RF) machine learning algorithm in assessing normal and malignant peripheral pulmonary nodules based on in vivo endobronchial optical coherence tomography (EB-OCT).

Multi-energy level fusion for nodal metastasis classification of primary lung tumor on dual energy CT using deep learning.

Computers in biology and medicine
Lymph node metastasis also called nodal metastasis (Nmet), is a clinically primary task for physicians. The survival and recurrence of lung cancer are related to the Nmet staging from Tumor-Node-Metastasis (TNM) reports. Furthermore, preoperative Nme...

Fully automatic pipeline of convolutional neural networks and capsule networks to distinguish COVID-19 from community-acquired pneumonia via CT images.

Computers in biology and medicine
BACKGROUND: Chest computed tomography (CT) is crucial in the diagnosis of coronavirus disease 2019 (COVID-19). However, the persistent pandemic and similar CT manifestations between COVID-19 and community-acquired pneumonia (CAP) raise methodological...

Brain CT registration using hybrid supervised convolutional neural network.

Biomedical engineering online
BACKGROUND: Image registration is an essential step in the automated interpretation of the brain computed tomography (CT) images of patients with acute cerebrovascular disease (ACVD). However, performing brain CT registration accurately and rapidly r...

Development of computer-aided model to differentiate COVID-19 from pulmonary edema in lung CT scan: EDECOVID-net.

Computers in biology and medicine
The efforts made to prevent the spread of COVID-19 face specific challenges in diagnosing COVID-19 patients and differentiating them from patients with pulmonary edema. Although systemically administered pulmonary vasodilators and acetazolamide are o...

A Rapid Artificial Intelligence-Based Computer-Aided Diagnosis System for COVID-19 Classification from CT Images.

Behavioural neurology
The excessive number of COVID-19 cases reported worldwide so far, supplemented by a high rate of false alarms in its diagnosis using the conventional polymerase chain reaction method, has led to an increased number of high-resolution computed tomogra...

Using Machine Learning to Identify Intravenous Contrast Phases on Computed Tomography.

Computer methods and programs in biomedicine
PURPOSE: The purpose of the present work is to demonstrate the application of machine learning (ML) techniques to automatically identify the presence and physiologic phase of intravenous (IV) contrast in Computed Tomography (CT) scans of the Chest, A...

Quantitative computed tomography imaging-based classification of cement dust-exposed subjects with an artificial neural network technique.

Computers in biology and medicine
BACKGROUND AND OBJECTIVE: Cement dust exposure is likely to affect the structural and functional alterations in segmental airways and parenchymal lungs. This study develops an artificial neural network (ANN) model for identifying cement dust-exposed ...