Assessing Coronary Arterial Dynamics from Cineangiograms

2013 ◽  
Vol 385-386 ◽  
pp. 631-634
Author(s):  
Zheng Sun ◽  
Qi Xiang Gao
Keyword(s):  
Image Data ◽  
Image Sequences ◽  
Experimental Results ◽  
Displacement Fields ◽  
Temporal Interval ◽  
Displacement Vectors ◽  
Coronary Arterial Dynamics ◽  
Skeleton Point

Assessing displacement fields of coronary arterial skeletons from a pair of nearly orthogonal cineangiographic image sequences is addressed. Projections of vessel skeletons are firstly extracted from each frame. Then, 2D displacement vectors of vessel skeletons during each temporal interval are estimated along the image sequences. 3D displacement vectors of each skeleton point are finally reconstructed along the overall sequences. Possible errors are discussed. Experimental results with clinically acquired in vivo image data have demonstrated the validity of the method.

scholarly journals Two-step machine learning method for the rapid analysis of microvascular flow in intravital video microscopy

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ossama Mahmoud ◽  
Mahmoud El-Sakka ◽  
Barry G. H. Janssen
Keyword(s):  
Machine Learning ◽  
Blood Flow ◽  
Human Error ◽  
Image Data ◽  
Video Microscopy ◽  
Microvascular Blood Flow ◽  
Image Processing Algorithm ◽  
Step Algorithm ◽  
3D Cnn

AbstractMicrovascular blood flow is crucial for tissue and organ function and is often severely affected by diseases. Therefore, investigating the microvasculature under different pathological circumstances is essential to understand the role of the microcirculation in health and sickness. Microvascular blood flow is generally investigated with Intravital Video Microscopy (IVM), and the captured images are stored on a computer for later off-line analysis. The analysis of these images is a manual and challenging process, evaluating experiments very time consuming and susceptible to human error. Since more advanced digital cameras are used in IVM, the experimental data volume will also increase significantly. This study presents a new two-step image processing algorithm that uses a trained Convolutional Neural Network (CNN) to functionally analyze IVM microscopic images without the need for manual analysis. While the first step uses a modified vessel segmentation algorithm to extract the location of vessel-like structures, the second step uses a 3D-CNN to assess whether the vessel-like structures have blood flowing in it or not. We demonstrate that our two-step algorithm can efficiently analyze IVM image data with high accuracy (83%). To our knowledge, this is the first application of machine learning for the functional analysis of microvascular blood flow in vivo.

scholarly journals Integrated experimental-computational analysis of a liver-islet microphysiological system for human-centric diabetes research

2021 ◽  
Author(s):  
Belén Casas ◽  
Liisa Vilén ◽  
Sophie Bauer ◽  
Kajsa Kanebratt ◽  
Charlotte Wennberg Huldt ◽  
...  
Keyword(s):  
Glucose Metabolism ◽  
Computational Model ◽  
In Silico ◽  
Experimental Results ◽  
Published Data ◽  
Diabetes Research ◽  
Glucose Response ◽  
High Blood Glucose

Microphysiological systems (MPS) are powerful tools for emulating human physiology and replicating disease progression in vitro. MPS could be better predictors of human outcome than current animal models, but mechanistic interpretation and in vivo extrapolation of the experimental results remain significant challenges. Here, we address these challenges using an integrated experimental-computational approach. This approach allows for in silico representation and predictions of glucose metabolism in a previously reported MPS with two organ compartments (liver and pancreas) connected in a closed loop with circulating medium. We developed a computational model describing glucose metabolism over 15 days of culture in the MPS. The model was calibrated on an experiment-specific basis using data from seven experiments, where single-liver or liver-islet cultures were exposed to both normal and hyperglycemic conditions resembling high blood glucose levels in diabetes. The calibrated models reproduced the fast (i.e. hourly) variations in glucose and insulin observed in the MPS experiments, as well as the long-term (i.e. over weeks) decline in both glucose tolerance and insulin secretion. We also investigated the behavior of the system under hypoglycemia by simulating this condition in silico, and the model could correctly predict the glucose and insulin responses measured in new MPS experiments. Last, we used the computational model to translate the experimental results to humans, showing good agreement with published data of the glucose response to a meal in healthy subjects. The integrated experimental-computational framework opens new avenues for future investigations toward disease mechanisms and the development of new therapies for metabolic disorders.

Cerebral arterial spasm

1974 ◽  
Vol 40 (4) ◽  
pp. 451-458 ◽  
Author(s):  
George S. Allen ◽  
Lawrence H. A. Gold ◽  
Shelley N. Chou ◽  
Lyle A. French
Keyword(s):  
Experimental Results ◽  
Arterial Spasm ◽  
Entire Study ◽  
Content Type ◽  
Fine Print

✓ In vivo experiments in dogs demonstrated that physiological concentrations of serotonin, when injected intracisternally, caused cerebral arterial spasm that lasted for at least 3 hours. Comparable spasm was produced by the injection of blood containing approximately the same amount of serotonin. Phenoxybenzamine reversed both the spasm produced by pure serotonin and that produced by blood. A hypothesis of the etiology of cerebral arterial spasm is proposed based on the experimental results of the entire study.

Measuring Vehicle Queue Length with Video

2011 ◽  
Vol 179-180 ◽  
pp. 109-114
Author(s):  
Zhong Qin ◽  
Guang Ting Su ◽  
Yi Chen ◽  
Qi Zhou Liu ◽  
Min Huang
Keyword(s):  
Traffic Control ◽  
Queue Length ◽  
Image Sequences ◽  
Urban Traffic ◽  
Experimental Results ◽  
Signal Control ◽  
Detection Algorithms ◽  
Intersection Signal Control ◽  
Urban Traffic Control ◽  
Stop Line

Queue length behind the stop line is an important parameter in the model of intersection signal control which is the base of urban traffic control. In this paper, the detection algorithms of queue length by the image information are proposed. At first, the background differential is used to extract the vehicle after the stop line, and then the three regional of the left, straight and right are identified, and finally at the different regions, tail of the vehicles queue is detected based on the change of image sequences gray, so the queue length is measured. The experimental results confirmed the effectiveness of the algorithm.

An Agar and Saline Phantom for Perfused Tissue

2003 ◽  
Author(s):  
Marie C. Madden ◽  
Elaine P. Scott
Keyword(s):  
Biomedical Research ◽  
Experimental Results ◽  
Tissue Phantom ◽  
Time Period ◽  
Directional Movement

A simple and accurate perfused tissue phantom would be a useful tool for biomedical research. The phantom described here is an agar-saline model that can be easily constructed. Space between finely diced pieces of agar is perfused with saline. A rotator Boekel-Orbitron® is used to perfuse the agarsaline mixture, mimicking the non-directional movement of blood. Dye injected into the phantom records the volumetric movement of saline for a set time period on the Orbitron. These data are used to determine perfusion estimates in units of g/s/mL. Experimental results can be appropriately scaled and additional modifications can be better mimic specific in-vivo conditions.

Comparison of Regional Lung Deformation Between Dynamic and Static CT Imagery Using Inverse Consistent Registration

2009 ◽  
Author(s):  
Ryan Amelon ◽  
Kai Ding ◽  
Kunlin Cao ◽  
Gary E. Christensen ◽  
Joseph M. Reinhardt ◽  
...  
Keyword(s):  
Image Data ◽  
Lung Mechanics ◽  
Ct Scans ◽  
Positive Pressure ◽  
Displacement Fields ◽  
Regional Patterns ◽  
Recent Developments ◽  
The Difference ◽  
Key Aspects ◽  
Lung Deformation

The mechanics of lung deformation is traditionally assessed at a whole-lung or lobar level. We submit that key aspects of lung mechanics maybe better understood by studying regional patterns of lung deformation by leveraging recent developments in tomographic imaging and image processing techniques. Our group has developed an inverse consistent registration technique for estimating local displacement distributions from paired lung CT volumes [1,2]. This facilitates the estimation of strain distributions and consequently, the regional patterns in volume change and its preferential directionalities (anisotropy in deformation). In this study, we use this novel method to compare regional deformation in the lungs between static and dynamic inflations in an adult sheep. Much of our research has focused on registration of static lung images at different positive end-expiratory pressures (PEEP). More recently, respiratory-gated CT scans of supine, positive-pressure inflated sheep lungs have been gathered in order to compare the displacement fields of a dynamically inflating lung to the static lung scans. The theory is that scanning a dynamically inflating lung will more accurately reflect natural deformation during breathing by realizing time-dependent mechanical properties (viscoelasticity). The downside to human dynamic lung imaging is the increased radiation dose necessary to acquire the image data across the respiratory cycle, though low-dose CT scans are an option [3]. This experiment observed the difference in strain distribution between dynamically inflated lungs versus static apneic lungs using the inverse consistent image registration developed in our lab.

Multi-Instance Learning for Image Retrieval with Relevance Feedback

2013 ◽  
Vol 427-429 ◽  
pp. 1606-1609 ◽  
Author(s):  
Tao Chen ◽  
Hui Fang Deng
Keyword(s):  
Image Retrieval ◽  
Objective Function ◽  
Relevance Feedback ◽  
Image Data ◽  
Experimental Results ◽  
Data Sets ◽  
Digital Repository ◽  
Novel Method

In this paper, we propose a novel method for image retrieval based on multi-instance learning with relevance feedback. The process of this method mainly includes the following three steps: First, it segments each image into a number of regions, treats images and regions as bags and instances respectively. Second, it constructs an objective function of multi-instance learning with the query images, which is used to rank the images from a large digital repository according to the distance values between the nearest region vector of each image and the maximum of the objective function. Third, based on the users relevance feedback, several rounds may be needed to refine the output images and their ranks. Finally, a satisfying set of images will be returned to users. Experimental results on COREL image data sets have demonstrated the effectiveness of the proposed approach.

FACE DETECTION FROM GREYSCALE IMAGES USING DETAILS FROM CATEGORIZED WAVELET COEFFICIENTS AS FEATURES FOR A DYNAMIC SUPERVISED FORWARD PROPAGATION NETWORK

2009 ◽  
Vol 23 (01) ◽  
pp. 3-15 ◽  
Author(s):  
LEE SENG YEONG ◽  
LI-MINN ANG ◽  
KING HANN LIM ◽  
KAH PHOOI SENG
Keyword(s):  
Face Detection ◽  
Detection Rate ◽  
Image Data ◽  
False Positives ◽  
Experimental Results ◽  
Training Data ◽  
Correct Detection ◽  
Wavelet Coefficients ◽  
Propagation Network ◽  
Counterpropagation Network

A dynamic counterpropagation network based on the forward only counterpropagation network (CPN) is applied as the classifier for face detection. The network, called the dynamic supervised forward-propagation network (DSFPN) trains using a supervised algorithm that grows dynamically during training allowing subclasses in the training data to be learnt. The network is trained using a reduced dimensionality categorized wavelet coefficients of the image data. Experimental results obtained show that a 94% correct detection rate can be achieved with less than 6% false positives.

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