AIMC Topic: Image Processing, Computer-Assisted

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Machine Learning and Deep Learning Applications in Magnetic Particle Imaging.

Journal of magnetic resonance imaging : JMRI
In recent years, magnetic particle imaging (MPI) has emerged as a promising imaging technique depicting high sensitivity and spatial resolution. It originated in the early 2000s where it proposed a new approach to challenge the low spatial resolution...

Learning CT-free attenuation-corrected total-body PET images through deep learning.

European radiology
OBJECTIVES: Total-body PET/CT scanners with long axial fields of view have enabled unprecedented image quality and quantitative accuracy. However, the ionizing radiation from CT is a major issue in PET imaging, which is more evident with reduced radi...

Image2InChI: Automated Molecular Optical Image Recognition.

Journal of chemical information and modeling
The accurate identification and analysis of chemical structures in molecular images are prerequisites of artificial intelligence for drug discovery. It is important to efficiently and automatically convert molecular images into machine-readable repre...

A Review of deep learning methods for denoising of medical low-dose CT images.

Computers in biology and medicine
To prevent patients from being exposed to excess of radiation in CT imaging, the most common solution is to decrease the radiation dose by reducing the X-ray, and thus the quality of the resulting low-dose CT images (LDCT) is degraded, as evidenced b...

Attenuation correction and truncation completion for breast PET/MR imaging using deep learning.

Physics in medicine and biology
. Simultaneous PET/MR scanners combine the high sensitivity of MR imaging with the functional imaging of PET. However, attenuation correction of breast PET/MR imaging is technically challenging. The purpose of this study is to establish a robust atte...

Training of a deep learning based digital subtraction angiography method using synthetic data.

Medical physics
BACKGROUND: Digital subtraction angiography (DSA) is a fluoroscopy method primarily used for the diagnosis of cardiovascular diseases (CVDs). Deep learning-based DSA (DDSA) is developed to extract DSA-like images directly from fluoroscopic images, wh...

UDRSNet: An unsupervised deformable registration module based on image structure similarity.

Medical physics
BACKGROUND: Image registration is a challenging problem in many clinical tasks, but deep learning has made significant progress in this area over the past few years. Real-time and robust registration has been made possible by supervised transformatio...

TFCNet: A texture-aware and fine-grained feature compensated polyp detection network.

Computers in biology and medicine
PURPOSE: Abnormal tissue detection is a prerequisite for medical image analysis and computer-aided diagnosis and treatment. The use of neural networks (CNN) to achieve accurate detection of intestinal polyps is beneficial to the early diagnosis and t...

Efficient leukocytes detection and classification in microscopic blood images using convolutional neural network coupled with a dual attention network.

Computers in biology and medicine
Leukocytes, also called White Blood Cells (WBCs) or leucocytes, are the cells that play a pivotal role in human health and are vital indicators of diseases such as malaria, leukemia, AIDS, and other viral infections. WBCs detection and classification...

A Spiking Artificial Vision Architecture Based on Fully Emulating the Human Vision.

Advanced materials (Deerfield Beach, Fla.)
Intelligent vision necessitates the deployment of detectors that are always-on and low-power, mirroring the continuous and uninterrupted responsiveness characteristic of human vision. Nonetheless, contemporary artificial vision systems attain this go...