AIMC Topic: Tomography, X-Ray Computed

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Deep Learning Algorithm for Automated Segmentation and Volume Measurement of the Liver and Spleen Using Portal Venous Phase Computed Tomography Images.

Korean journal of radiology
OBJECTIVE: Measurement of the liver and spleen volumes has clinical implications. Although computed tomography (CT) volumetry is considered to be the most reliable noninvasive method for liver and spleen volume measurement, it has limited application...

Automated Measurements of Muscle Mass Using Deep Learning Can Predict Clinical Outcomes in Patients With Liver Disease.

The American journal of gastroenterology
INTRODUCTION: There is increasing recognition of the central role of muscle mass in predicting clinical outcomes in patients with liver disease. Muscle size can be extracted from computed tomography (CT) scans, but clinical implementation will requir...

A Unique Signature of Cardiac-Induced Cranial Forces During Acute Large Vessel Stroke and Development of a Predictive Model.

Neurocritical care
BACKGROUND: Cranial accelerometry is used to detect cerebral vasospasm and concussion. We explored this technique in a cohort of code stroke patients to see whether a signature could be identified to aid in the diagnosis of large vessel occlusion (LV...

Assessing the Accuracy of a Deep Learning Method to Risk Stratify Indeterminate Pulmonary Nodules.

American journal of respiratory and critical care medicine
The management of indeterminate pulmonary nodules (IPNs) remains challenging, resulting in invasive procedures and delays in diagnosis and treatment. Strategies to decrease the rate of unnecessary invasive procedures and optimize surveillance regime...

Deformable US/CT Image Registration with a Convolutional Neural Network for Cardiac Arrhythmia Therapy.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Image registration represents one of the fundamental techniques in medical imaging and image-guided interventions. In this paper, we present a Convolutional Neural Network (CNN) framework for deformable transesophageal US/CT image registration, for t...

Limited-Angle Computed Tomography Reconstruction using Combined FDK-Based Neural Network and U-Net.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The limited-angle cone-beam Computed Tomography (CT) is often used in C-arm for clinical diagnosis with the advantages of cheap cost and radiation dose reduction. However, due to incomplete projection data, the 3-dimensional CT images reconstructed b...

Deep learning approaches for bone and bone lesion segmentation on 18FDG PET/CT imaging in the context of metastatic breast cancer.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
FDG PET/CT imaging is commonly used in diagnosis and follow-up of metastatic breast cancer, but its quantitative analysis is complicated by the number and location heterogeneity of metastatic lesions. Considering that bones are the most common locati...

Lung CT Image Registration through Landmark-constrained Learning with Convolutional Neural Network.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Accurate registration of lung computed tomography (CT) image is a significant task in thorax image analysis. Recently deep learning-based medical image registration methods develop fast and achieve promising performance on accuracy and speed. However...

Evaluating Deep Learning Algorithms in Pulmonary Nodule Detection.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Lung cancer is considered the deadliest cancer worldwide. In order to detect it, radiologists need to inspect multiple Computed Tomography (CT) scans. This task is tedious and time consuming. In recent years, promising methods based on deep learning ...

Metal Artifacts Reduction in CT Scans using Convolutional Neural Network with Ground Truth Elimination.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Metal artifacts are very common in CT scans since metal insertion or replacement is performed for enhancing certain functionality or mechanism of patient's body. These streak artifacts could degrade CT image quality severely, and consequently, they c...