AIMC Topic: Image Processing, Computer-Assisted

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Current Status and Future Perspectives of Artificial Intelligence in Magnetic Resonance Breast Imaging.

Contrast media & molecular imaging
Recent advances in artificial intelligence (AI) and deep learning (DL) have impacted many scientific fields including biomedical maging. Magnetic resonance imaging (MRI) is a well-established method in breast imaging with several indications includin...

Automated feature detection in dental periapical radiographs by using deep learning.

Oral surgery, oral medicine, oral pathology and oral radiology
OBJECTIVE: The aim of this study was to investigate automated feature detection, segmentation, and quantification of common findings in periapical radiographs (PRs) by using deep learning (DL)-based computer vision techniques.

Clinically applicable histopathological diagnosis system for gastric cancer detection using deep learning.

Nature communications
The early detection and accurate histopathological diagnosis of gastric cancer increase the chances of successful treatment. The worldwide shortage of pathologists offers a unique opportunity for the use of artificial intelligence assistance systems ...

Improving depth-of-interaction resolution in pixellated PET detectors using neural networks.

Physics in medicine and biology
Parallax error is a common issue in high-resolution preclinical positron emission tomography (PET) scanners as well as in clinical scanners that have a long axial field of view (FOV), which increases estimation uncertainty of the annihilation positio...

Robustness study of noisy annotation in deep learning based medical image segmentation.

Physics in medicine and biology
Partly due to the use of exhaustive-annotated data, deep networks have achieved impressive performance on medical image segmentation. Medical imaging data paired with noisy annotation are, however, ubiquitous, but little is known about the effect of ...

Neuroimaging and deep learning for brain stroke detection - A review of recent advancements and future prospects.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: In recent years, deep learning algorithms have created a massive impact on addressing research challenges in different domains. The medical field also greatly benefits from the use of improving deep learning models which sav...

Spatial-channel relation learning for brain tumor segmentation.

Medical physics
PURPOSE: Recently, research on brain tumor segmentation has made great progress. However, ambiguous patterns in magnetic resonance imaging data and linear fusion omitting semantic gaps between features in different branches remain challenging. We nee...

A learning-based method for online adjustment of C-arm Cone-beam CT source trajectories for artifact avoidance.

International journal of computer assisted radiology and surgery
PURPOSE: During spinal fusion surgery, screws are placed close to critical nerves suggesting the need for highly accurate screw placement. Verifying screw placement on high-quality tomographic imaging is essential. C-arm cone-beam CT (CBCT) provides ...

Real-time deep learning-based image recognition for applications in automated positioning and injection of biological cells.

Computers in biology and medicine
Biological cell injection is an effective method in which a foreign material is directly introduced into a biological cell. Since human involvement reduces the success rate of the biological microinjection procedure, an extensive research effort has ...

Change Detection of Remote Sensing Images Based on Attention Mechanism.

Computational intelligence and neuroscience
In recent years, image processing methods based on convolutional neural networks (CNNs) have achieved very good results. At the same time, many branch techniques have been proposed to improve accuracy. Aiming at the change detection task of remote se...