AIMC Topic: Image Enhancement
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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 ...
DeepPET: A deep encoder-decoder network for directly solving the PET image reconstruction inverse problem.
Medical image analysis
Mar 30, 2019
The purpose of this research was to implement a deep learning network to overcome two of the major bottlenecks in improved image reconstruction for clinical positron emission tomography (PET). These are the lack of an automated means for the optimiza...
Robust segmentation of arterial walls in intravascular ultrasound images using Dual Path U-Net.
Ultrasonics
Mar 23, 2019
A Fully Convolutional Network (FCN) based deep architecture called Dual Path U-Net (DPU-Net) is proposed for automatic segmentation of the lumen and media-adventitia in IntraVascular UltraSound (IVUS) frames, which is crucial for diagnosis of many ca...
Low-light image enhancement of high-speed endoscopic videos using a convolutional neural network.
Medical & biological engineering & computing
Mar 21, 2019
Laryngeal endoscopy is one of the primary diagnostic tools for laryngeal disorders. The main techniques are videostroboscopy and lately high-speed video endoscopy. Unfortunately, due to the restricting anatomy of the larynx and technical limitations ...
Deep learning-based super-resolution in coherent imaging systems.
Scientific reports
Mar 8, 2019
We present a deep learning framework based on a generative adversarial network (GAN) to perform super-resolution in coherent imaging systems. We demonstrate that this framework can enhance the resolution of both pixel size-limited and diffraction-lim...
MITK Phenotyping: An open-source toolchain for image-based personalized medicine with radiomics.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Dec 31, 2018
Radiomics - The extraction of quantitative features from radiologic images - shows increasing potential in contributing to modern personalized medicine approaches. MITK Phenotyping is an openly distributed radiomics framework implementing an exhausti...
P_VggNet: A convolutional neural network (CNN) with pixel-based attention map.
PloS one
Dec 12, 2018
Attention maps have been fused in the VggNet structure (EAC-Net) [1] and have shown significant improvement compared to that of the VggNet structure. However, in [1], E-Net was designed based on the facial action unit (AU) center and for facial AU de...
Development and testing of a deep learning-based strategy for scar segmentation on CMR-LGE images.
Magma (New York, N.Y.)
Nov 20, 2018
OBJECTIVE: The aim of this paper is to investigate the use of fully convolutional neural networks (FCNNs) to segment scar tissue in the left ventricle from cardiac magnetic resonance with late gadolinium enhancement (CMR-LGE) images.
Deep transfer learning-based hologram classification for molecular diagnostics.
Scientific reports
Nov 19, 2018
Lens-free digital in-line holography (LDIH) is a promising microscopic tool that overcomes several drawbacks (e.g., limited field of view) of traditional lens-based microcopy. However, extensive computation is required to reconstruct object images fr...
Multi-sequence myocardium segmentation with cross-constrained shape and neural network-based initialization.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Nov 15, 2018
For myocardial infarction (MI) patients, delayed enhancement (DE) and T2-weighted cardiovascular magnetic resonance imaging (CMR) can play significant roles in diagnosis, prognosis and therapeutic strategy evaluation. However, the non-rigid registrat...
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