AIMC Topic: Image Processing, Computer-Assisted

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Localization of common carotid artery transverse section in B-mode ultrasound images using faster RCNN: a deep learning approach.

Medical & biological engineering & computing
Cardiologists can acquire important information related to patients' cardiac health using carotid artery stiffness, its lumen diameter (LD), and its carotid intima-media thickness (cIMT). The sonographers primarily concern about the location of the a...

Multimodality image registration in the head-and-neck using a deep learning-derived synthetic CT as a bridge.

Medical physics
PURPOSE: To develop and demonstrate the efficacy of a novel head-and-neck multimodality image registration technique using deep-learning-based cross-modality synthesis.

A Multidimensional Neural Maturation Index Reveals Reproducible Developmental Patterns in Children and Adolescents.

The Journal of neuroscience : the official journal of the Society for Neuroscience
Adolescence is a time of extensive neural restructuring, leaving one susceptible to atypical development. Although neural maturation in humans can be measured using functional and structural MRI, the subtle patterns associated with the initial stages...

A new deep learning method for image deblurring in optical microscopic systems.

Journal of biophotonics
Deconvolution is the most commonly used image processing method in optical imaging systems to remove the blur caused by the point-spread function (PSF). While this method has been successful in deblurring, it suffers from several disadvantages, such ...

Automatic multi-organ segmentation in dual-energy CT (DECT) with dedicated 3D fully convolutional DECT networks.

Medical physics
PURPOSE: Dual-energy computed tomography (DECT) has shown great potential in many clinical applications. By incorporating the information from two different energy spectra, DECT provides higher contrast and reveals more material differences of tissue...

Semantic segmentation with DenseNets for carotid artery ultrasound plaque segmentation and CIMT estimation.

Artificial intelligence in medicine
BACKGROUND AND OBJECTIVE: The measurement of carotid intima media thickness (CIMT) in ultrasound images can be used to detect the presence of atherosclerotic plaques. Usually, the CIMT estimation strategy is semi-automatic, since it requires: (1) a m...

A Multichannel 2D Convolutional Neural Network Model for Task-Evoked fMRI Data Classification.

Computational intelligence and neuroscience
Deep learning models have been successfully applied to the analysis of various functional MRI data. Convolutional neural networks (CNN), a class of deep neural networks, have been found to excel at extracting local meaningful features based on their ...

A novel transcranial ultrasound imaging method with diverging wave transmission and deep learning approach.

Computer methods and programs in biomedicine
Real time brain transcranial ultrasound imaging is extremely intriguing because of its numerous applications. However, the skull causes phase distortion and amplitude attenuation of ultrasound signals due to its density: the speed of sound is signifi...

When Deep Learning Meets Cell Image Synthesis.

Cytometry. Part A : the journal of the International Society for Analytical Cytology

Breast tumor classification through learning from noisy labeled ultrasound images.

Medical physics
PURPOSE: To train deep learning models to differentiate benign and malignant breast tumors in ultrasound images, we need to collect many training samples with clear labels. In general, biopsy results can be used as benign/malignant labels. However, m...