AIMC Topic: Image Processing, Computer-Assisted

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Learning-based algorithms for vessel tracking: A review.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Developing efficient vessel-tracking algorithms is crucial for imaging-based diagnosis and treatment of vascular diseases. Vessel tracking aims to solve recognition problems such as key (seed) point detection, centerline extraction, and vascular segm...

Identify abnormal functional connectivity of resting state networks in Autism spectrum disorder and apply to machine learning-based classification.

Brain research
Autism spectrum disorder (ASD) patients are often reported altered patterns of functional connectivity (FC) on resting-state functional magnetic resonance imaging (rsfMRI) scans. However, the results in similar brain regions were inconsistent. In thi...

DeepLabStream enables closed-loop behavioral experiments using deep learning-based markerless, real-time posture detection.

Communications biology
In general, animal behavior can be described as the neuronal-driven sequence of reoccurring postures through time. Most of the available current technologies focus on offline pose estimation with high spatiotemporal resolution. However, to correlate ...

Deep convolutional neural networks to predict cardiovascular risk from computed tomography.

Nature communications
Coronary artery calcium is an accurate predictor of cardiovascular events. While it is visible on all computed tomography (CT) scans of the chest, this information is not routinely quantified as it requires expertise, time, and specialized equipment....

Towards Fine-Grained Human Pose Transfer With Detail Replenishing Network.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Human pose transfer (HPT) is an emerging research topic with huge potential in fashion design, media production, online advertising and virtual reality. For these applications, the visual realism of fine-grained appearance details is crucial for prod...

VC-Net: Deep Volume-Composition Networks for Segmentation and Visualization of Highly Sparse and Noisy Image Data.

IEEE transactions on visualization and computer graphics
The fundamental motivation of the proposed work is to present a new visualization-guided computing paradigm to combine direct 3D volume processing and volume rendered clues for effective 3D exploration. For example, extracting and visualizing microst...

Fast Multi-Focus Fusion Based on Deep Learning for Early-Stage Embryo Image Enhancement.

Sensors (Basel, Switzerland)
BACKGROUND: Cell detection and counting is of essential importance in evaluating the quality of early-stage embryo. Full automation of this process remains a challenging task due to different cell size, shape, the presence of incomplete cell boundari...

Dense cellular segmentation for EM using 2D-3D neural network ensembles.

Scientific reports
Biologists who use electron microscopy (EM) images to build nanoscale 3D models of whole cells and their organelles have historically been limited to small numbers of cells and cellular features due to constraints in imaging and analysis. This has be...

Joint optic disc and cup segmentation based on densely connected depthwise separable convolution deep network.

BMC medical imaging
BACKGROUND: Glaucoma is an eye disease that causes vision loss and even blindness. The cup to disc ratio (CDR) is an important indicator for glaucoma screening and diagnosis. Accurate segmentation for the optic disc and cup helps obtain CDR. Although...