AIMC Topic: Contrast Media
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Computer-aided detection and visualization of pulmonary embolism using a novel, compact, and discriminative image representation.
Medical image analysis
Aug 6, 2019
Diagnosing pulmonary embolism (PE) and excluding disorders that may clinically and radiologically simulate PE poses a challenging task for both human and machine perception. In this paper, we propose a novel vessel-oriented image representation (VOIR...
Performance of deep learning for differentiating pancreatic diseases on contrast-enhanced magnetic resonance imaging: A preliminary study.
Diagnostic and interventional imaging
Jul 30, 2019
PURPOSE: The purpose of this study was to evaluate the ability of deep learning to differentiate pancreatic diseases on contrast-enhanced magnetic resonance (MR) images with the aid of generative adversarial network (GAN).
Machine learning-based texture analysis for differentiation of large adrenal cortical tumours on CT.
Clinical radiology
Jul 27, 2019
AIM: To compare the efficacy of computed tomography (CT) texture analysis and conventional evaluation by radiologists for differentiation between large adrenal adenomas and carcinomas.
A Deep Information Sharing Network for Multi-Contrast Compressed Sensing MRI Reconstruction.
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Jul 9, 2019
Compressed sensing (CS) theory can accelerate multi-contrast magnetic resonance imaging (MRI) by sampling fewer measurements within each contrast. However, conventional optimization-based reconstruction models suffer several limitations, including a ...
SANTIS: Sampling-Augmented Neural neTwork with Incoherent Structure for MR image reconstruction.
Magnetic resonance in medicine
Jun 5, 2019
PURPOSE: To develop and evaluate a novel deep learning-based reconstruction framework called SANTIS (Sampling-Augmented Neural neTwork with Incoherent Structure) for efficient MR image reconstruction with improved robustness against sampling pattern ...
Automated Identification of Optimal Portal Venous Phase Timing with Convolutional Neural Networks.
Academic radiology
May 28, 2019
OBJECTIVES: To develop a deep learning-based algorithm to automatically identify optimal portal venous phase timing (PVP-timing) so that image analysis techniques can be accurately performed on post contrast studies.
Learning-based automatic segmentation of arteriovenous malformations on contrast CT images in brain stereotactic radiosurgery.
Medical physics
May 21, 2019
PURPOSE: Stereotactic radiosurgery (SRS) is widely used to obliterate arteriovenous malformations (AVMs). Its performance relies on the accuracy of delineating the target AVM. Manual segmentation during a framed SRS procedure is time consuming and su...
Model-free prostate cancer segmentation from dynamic contrast-enhanced MRI with recurrent convolutional networks: A feasibility study.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
May 19, 2019
Dynamic contrast enhanced (DCE) magnetic resonance imaging (MRI) is a method of temporal imaging that is commonly used to aid in prostate cancer (PCa) diagnosis and staging. Typically, machine learning models designed for the segmentation and detecti...
Machine learning for prediction of chemoradiation therapy response in rectal cancer using pre-treatment and mid-radiation multi-parametric MRI.
Magnetic resonance imaging
May 3, 2019
PURPOSE: To predict the neoadjuvant chemoradiation therapy (CRT) response in patients with locally advanced rectal cancer (LARC) using radiomics and deep learning based on pre-treatment MRI and a mid-radiation follow-up MRI taken 3-4 weeks after the ...
Dynamic contrast-enhanced computed tomography diagnosis of primary liver cancers using transfer learning of pretrained convolutional neural networks: Is registration of multiphasic images necessary?
International journal of computer assisted radiology and surgery
May 3, 2019
PURPOSE: To evaluate the effect of image registration on the diagnostic performance of transfer learning (TL) using pretrained convolutional neural networks (CNNs) and three-phasic dynamic contrast-enhanced computed tomography (DCE-CT) for primary li...
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