AIMC Topic: Image Processing, Computer-Assisted

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A Detailed Systematic Review on Retinal Image Segmentation Methods.

Journal of digital imaging
The separation of blood vessels in the retina is a major aspect in detecting ailment and is carried out by segregating the retinal blood vessels from the fundus images. Moreover, it helps to provide earlier therapy for deadly diseases and prevent fur...

Deep learning-based time-of-flight (ToF) image enhancement of non-ToF PET scans.

European journal of nuclear medicine and molecular imaging
PURPOSE: To improve the quantitative accuracy and diagnostic confidence of PET images reconstructed without time-of-flight (ToF) using deep learning models trained for ToF image enhancement (DL-ToF).

Registration-guided deep learning image segmentation for cone beam CT-based online adaptive radiotherapy.

Medical physics
PURPOSE: Adaptive radiotherapy (ART), especially online ART, effectively accounts for positioning errors and anatomical changes. One key component of online ART process is accurately and efficiently delineating organs at risk (OARs) and targets on on...

Multilevel Structural Components Detection and Segmentation toward Computer Vision-Based Bridge Inspection.

Sensors (Basel, Switzerland)
Bridge inspection plays a critical role in mitigating the safety risks associated with bridge deterioration and decay. CV (computer vision) technology can facilitate bridge inspection by accurately automating the structural recognition tasks, especia...

Auxiliary Diagnosis of Lung Cancer with Magnetic Resonance Imaging Data under Deep Learning.

Computational and mathematical methods in medicine
This study was aimed at two image segmentation methods of three-dimensional (3D) U-shaped network (U-Net) and multilevel boundary sensing residual U-shaped network (RUNet) and their application values on the auxiliary diagnosis of lung cancer. In thi...

Adaptive Contrast for Image Regression in Computer-Aided Disease Assessment.

IEEE transactions on medical imaging
Image regression tasks for medical applications, such as bone mineral density (BMD) estimation and left-ventricular ejection fraction (LVEF) prediction, play an important role in computer-aided disease assessment. Most deep regression methods train t...

Deep Learning Based Joint PET Image Reconstruction and Motion Estimation.

IEEE transactions on medical imaging
Respiratory motion is one of the main sources of motion artifacts in positron emission tomography (PET) imaging. The emission image and patient motion can be estimated simultaneously from respiratory gated data through a joint estimation framework. H...

Unsupervised Deep Learning for FOD-Based Susceptibility Distortion Correction in Diffusion MRI.

IEEE transactions on medical imaging
Susceptibility induced distortion is a major artifact that affects the diffusion MRI (dMRI) data analysis. In the Human Connectome Project (HCP), the state-of-the-art method adopted to correct this kind of distortion is to exploit the displacement fi...

Deep-Learning-Based Automated Neuron Reconstruction From 3D Microscopy Images Using Synthetic Training Images.

IEEE transactions on medical imaging
Digital reconstruction of neuronal structures from 3D microscopy images is critical for the quantitative investigation of brain circuits and functions. It is a challenging task that would greatly benefit from automatic neuron reconstruction methods. ...

SinGAN-Seg: Synthetic training data generation for medical image segmentation.

PloS one
Analyzing medical data to find abnormalities is a time-consuming and costly task, particularly for rare abnormalities, requiring tremendous efforts from medical experts. Therefore, artificial intelligence has become a popular tool for the automatic p...