AIMC Topic: Tomography, X-Ray Computed

Clear Filters Showing 3891 to 3900 of 5128 articles

Predicting EGFR mutation status in lung adenocarcinoma on computed tomography image using deep learning.

The European respiratory journal
Epidermal growth factor receptor (EGFR) genotyping is critical for treatment guidelines such as the use of tyrosine kinase inhibitors in lung adenocarcinoma. Conventional identification of EGFR genotype requires biopsy and sequence testing which is i...

Kidney cortex segmentation in 2D CT with U-Nets ensemble aggregation.

Diagnostic and interventional imaging
PURPOSE: This work presents our contribution to one of the data challenges organized by the French Radiology Society during the Journées Francophones de Radiologie. This challenge consisted in segmenting the kidney cortex from coronal computed tomogr...

Integrating spatial configuration into heatmap regression based CNNs for landmark localization.

Medical image analysis
In many medical image analysis applications, only a limited amount of training data is available due to the costs of image acquisition and the large manual annotation effort required from experts. Training recent state-of-the-art machine learning met...

Coronary Calcium Detection Based on Improved Deep Residual Network in Mimics.

Journal of medical systems
Coronary calcium detection in medicine image processing is a hot research topic. According to the low resolution and complex background in medicine image, an improved coronary calcium detection algorithm based on the Single Shot MultiBox Detector (SS...

TeTrIS: Template Transformer Networks for Image Segmentation With Shape Priors.

IEEE transactions on medical imaging
In this paper, we introduce and compare different approaches for incorporating shape prior information into neural network-based image segmentation. Specifically, we introduce the concept of template transformer networks, where a shape template is de...

The best predictor of ischemic coronary stenosis: subtended myocardial volume, machine learning-based FFR, or high-risk plaque features?

European radiology
OBJECTIVES: The present study aimed to compare the diagnostic performance of a machine learning (ML)-based FFR algorithm, quantified subtended myocardial volume, and high-risk plaque features for predicting if a coronary stenosis is hemodynamically s...

Fast and fully-automated detection and segmentation of pulmonary nodules in thoracic CT scans using deep convolutional neural networks.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Deep learning techniques have been extensively used in computerized pulmonary nodule analysis in recent years. Many reported studies still utilized hybrid methods for diagnosis, in which convolutional neural networks (CNNs) are used only as one part ...

CT male pelvic organ segmentation using fully convolutional networks with boundary sensitive representation.

Medical image analysis
Accurate segmentation of the prostate and organs at risk (e.g., bladder and rectum) in CT images is a crucial step for radiation therapy in the treatment of prostate cancer. However, it is a very challenging task due to unclear boundaries, large intr...

Deep convolutional neural network for segmentation of thoracic organs-at-risk using cropped 3D images.

Medical physics
PURPOSE: Automatic segmentation of organs-at-risk (OARs) is a key step in radiation treatment planning to reduce human efforts and bias. Deep convolutional neural networks (DCNN) have shown great success in many medical image segmentation application...

Multi-scale gradual integration CNN for false positive reduction in pulmonary nodule detection.

Neural networks : the official journal of the International Neural Network Society
Lung cancer is a global and dangerous disease, and its early detection is crucial for reducing the risks of mortality. In this regard, it has been of great interest in developing a computer-aided system for pulmonary nodules detection as early as pos...