AIMC Topic: Image Processing, Computer-Assisted

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Deep learning based MRI contrast synthesis using full volume prediction using full volume prediction.

Biomedical physics & engineering express
In Magnetic Resonance Imaging (MRI), depending on the image acquisition settings, a large number of image types or contrasts can be generated showing complementary information of the same imaged subject. This multi-spectral information is highly bene...

Stable Deep Neural Network Architectures for Mitochondria Segmentation on Electron Microscopy Volumes.

Neuroinformatics
Electron microscopy (EM) allows the identification of intracellular organelles such as mitochondria, providing insights for clinical and scientific studies. In recent years, a number of novel deep learning architectures have been published reporting ...

The effect of a post-scan processing denoising system on image quality and morphometric analysis.

Journal of neuroradiology = Journal de neuroradiologie
PURPOSE: MR image quality and subsequent brain morphometric analysis are inevitably affected by noise. The purpose of this study was to evaluate the effectiveness of an artificial intelligence (AI)-based post-scan processing denoising system, intelli...

VBNet: An end-to-end 3D neural network for vessel bifurcation point detection in mesoscopic brain images.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Accurate detection of vessel bifurcation points from mesoscopic whole-brain images plays an important role in reconstructing cerebrovascular networks and understanding the pathogenesis of brain diseases. Existing detection m...

Development and Practical Implementation of a Deep Learning-Based Pipeline for Automated Pre- and Postoperative Glioma Segmentation.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Quantitative volumetric segmentation of gliomas has important implications for diagnosis, treatment, and prognosis. We present a deep-learning model that accommodates automated preoperative and postoperative glioma segmentatio...

Automatic upper airway segmentation in static and dynamic MRI via anatomy-guided convolutional neural networks.

Medical physics
PURPOSE: Upper airway segmentation on MR images is a prerequisite step for quantitatively studying the anatomical structure and function of the upper airway and surrounding tissues. However, the complex variability of intensity and shape of anatomica...

Real-Time Semantic Segmentation with Dual Encoder and Self-Attention Mechanism for Autonomous Driving.

Sensors (Basel, Switzerland)
As the techniques of autonomous driving become increasingly valued and universal, real-time semantic segmentation has become very popular and challenging in the field of deep learning and computer vision in recent years. However, in order to apply th...

Deep neural network for the determination of transformed foci in Bhas 42 cell transformation assay.

Scientific reports
Bhas 42 cell transformation assay (CTA) has been used to estimate the carcinogenic potential of chemicals by exposing Bhas 42 cells to carcinogenic stimuli to form colonies, referred to as transformed foci, on the confluent monolayer. Transformed foc...

A brain extraction algorithm for infant T2 weighted magnetic resonance images based on fuzzy c-means thresholding.

Scientific reports
It is challenging to extract the brain region from T2-weighted magnetic resonance infant brain images because conventional brain segmentation algorithms are generally optimized for adult brain images, which have different spatial resolution, dynamic ...

Identification of public submitted tick images: A neural network approach.

PloS one
Ticks and tick-borne diseases represent a growing public health threat in North America and Europe. The number of ticks, their geographical distribution, and the incidence of tick-borne diseases, like Lyme disease, are all on the rise. Accurate, real...