AIMC Topic: Image Processing, Computer-Assisted

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Brain CT registration using hybrid supervised convolutional neural network.

Biomedical engineering online
BACKGROUND: Image registration is an essential step in the automated interpretation of the brain computed tomography (CT) images of patients with acute cerebrovascular disease (ACVD). However, performing brain CT registration accurately and rapidly r...

Visual Perceptual Quality Assessment Based on Blind Machine Learning Techniques.

Sensors (Basel, Switzerland)
This paper presents the construction of a new objective method for estimation of visual perceiving quality. The proposal provides an assessment of image quality without the need for a reference image or a specific distortion assumption. Two main proc...

Evaluation on the generalization of a learned convolutional neural network for MRI reconstruction.

Magnetic resonance imaging
Recently, deep learning approaches with various network architectures have drawn significant attention from the magnetic resonance imaging (MRI) community because of their great potential for image reconstruction from undersampled k-space data in fas...

A cross-scanner and cross-tracer deep learning method for the recovery of standard-dose imaging quality from low-dose PET.

European journal of nuclear medicine and molecular imaging
PURPOSE: A critical bottleneck for the credibility of artificial intelligence (AI) is replicating the results in the diversity of clinical practice. We aimed to develop an AI that can be independently applied to recover high-quality imaging from low-...

Recognition of Maize Phenology in Sentinel Images with Machine Learning.

Sensors (Basel, Switzerland)
The scarcity of water for agricultural use is a serious problem that has increased due to intense droughts, poor management, and deficiencies in the distribution and application of the resource. The monitoring of crops through satellite image process...

A review on deep learning MRI reconstruction without fully sampled k-space.

BMC medical imaging
BACKGROUND: Magnetic resonance imaging (MRI) is an effective auxiliary diagnostic method in clinical medicine, but it has always suffered from the problem of long acquisition time. Compressed sensing and parallel imaging are two common techniques to ...

NoAS-DS: Neural optimal architecture search for detection of diverse DNA signals.

Neural networks : the official journal of the International Neural Network Society
Neural network architectures are high-performing variable models that can solve many learning tasks. Designing architectures manually require substantial time and also prior knowledge and expertise to develop a high-accuracy model. Most of the archit...

A deep-learning framework for metacarpal-head cartilage-thickness estimation in ultrasound rheumatological images.

Computers in biology and medicine
OBJECTIVE: Rheumatoid arthritis (RA) is a chronic disease characterized by erosive symmetrical polyarthritis. Bone and cartilage are the main joint targets of this disease. Cartilage damage is one of the most relevant determinants of physical disabil...

Comparison of Semi-Quantitative Scoring and Artificial Intelligence Aided Digital Image Analysis of Chromogenic Immunohistochemistry.

Biomolecules
Semi-quantitative scoring is a method that is widely used to estimate the quantity of proteins on chromogen-labelled immunohistochemical (IHC) tissue sections. However, it suffers from several disadvantages, including its lack of objectivity and the ...

DEep learning-based rapid Spiral Image REconstruction (DESIRE) for high-resolution spiral first-pass myocardial perfusion imaging.

NMR in biomedicine
The objective of the current study was to develop and evaluate a DEep learning-based rapid Spiral Image REconstruction (DESIRE) technique for high-resolution spiral first-pass myocardial perfusion imaging with whole-heart coverage, to provide fast an...