AIMC Topic: Tomography, X-Ray Computed

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Artificial intelligence and radiomics in pulmonary nodule management: current status and future applications.

Clinical radiology
Artificial intelligence (AI) has been present in some guise within the field of radiology for over 50 years. The first studies investigating computer-aided diagnosis in thoracic radiology date back to the 1960s, and in the subsequent years, the main ...

Fusing learned representations from Riesz Filters and Deep CNN for lung tissue classification.

Medical image analysis
A novel method to detect and classify several classes of diseased and healthy lung tissue in CT (Computed Tomography), based on the fusion of Riesz and deep learning features, is presented. First, discriminative parametric lung tissue texture signatu...

MRI-only based synthetic CT generation using dense cycle consistent generative adversarial networks.

Medical physics
PURPOSE: Automated synthetic computed tomography (sCT) generation based on magnetic resonance imaging (MRI) images would allow for MRI-only based treatment planning in radiation therapy, eliminating the need for CT simulation and simplifying the pati...

A cascaded dual-pathway residual network for lung nodule segmentation in CT images.

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)
It is difficult to obtain an accurate segmentation due to the variety of lung nodules in computed tomography (CT) images. In this study, we propose a data-driven model, called the Cascaded Dual-Pathway Residual Network (CDP-ResNet) to improve the seg...

Enabling machine learning in X-ray-based procedures via realistic simulation of image formation.

International journal of computer assisted radiology and surgery
PURPOSE: Machine learning-based approaches now outperform competing methods in most disciplines relevant to diagnostic radiology. Image-guided procedures, however, have not yet benefited substantially from the advent of deep learning, in particular b...

Machine learning approach for distinguishing malignant and benign lung nodules utilizing standardized perinodular parenchymal features from CT.

Medical physics
PURPOSE: Computed tomography (CT) is an effective method for detecting and characterizing lung nodules in vivo. With the growing use of chest CT, the detection frequency of lung nodules is increasing. Noninvasive methods to distinguish malignant from...

Comparison between navigated reported position and postoperative computed tomography to evaluate accuracy in a robotic navigation system in total knee arthroplasty.

The Knee
BACKGROUND: Computer navigation increases reproducibility compared to non-navigated total knee arthroplasty (TKA). Robotics navigation is a branch of computer navigation technology that might further improve accuracy of implant placement. The aim of ...

Deep Learning-Based Automatic Segmentation of Lumbosacral Nerves on CT for Spinal Intervention: A Translational Study.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: 3D reconstruction of a targeted area ("safe" triangle and Kambin triangle) may benefit the viability assessment of transforaminal epidural steroid injection, especially at the L5/S1 level. However, manual segmentation of lumbo...

Microaneurysm detection in fundus images using a two-step convolutional neural network.

Biomedical engineering online
BACKGROUND AND OBJECTIVES: Diabetic retinopathy (DR) is the leading cause of blindness worldwide, and therefore its early detection is important in order to reduce disease-related eye injuries. DR is diagnosed by inspecting fundus images. Since micro...