scholarly journals A rapid, nondestructive method for vascular network visualization

BioTechniques ◽  
2020 ◽  
Vol 69 (6) ◽  
pp. 443-449
Author(s):  
Austin Veith ◽  
Aaron B Baker
Keyword(s):  
Blood Vessel ◽  
Learning Algorithm ◽  
Three Dimensional ◽  
Nondestructive Method ◽  
Vascular Network ◽  
Micro Computed Tomography ◽  
Nondestructive Technique ◽  
Fixed Tissue ◽  
Animal Models Of Disease ◽  
Deep Learning Algorithm

The quantitative analysis of blood vessel networks is an important component in many animal models of disease. We describe a nondestructive technique for blood vessel imaging that visualizes in situ vasculature in harvested tissues. The method allows for further analysis of the same tissues with histology and other methods that can be performed on fixed tissue. Consequently, it can easily be incorporated upstream to analysis methods to augment these with a three-dimensional reconstruction of the vascular network in the tissues to be analyzed. The method combines iodine-enhanced micro-computed tomography with a deep learning algorithm to segment vasculature within tissues. The procedure is relatively simple and can provide insight into complex changes in the vascular structure in the tissues.

Evaluation and Analysis of Cardiovascular Function in Intensive Care Unit Patients by Ultrasound Image Segmentation Based on Deep Learning

2020 ◽  
Vol 10 (8) ◽  
pp. 1892-1898
Author(s):  
Jiaqi Shen ◽  
Fangfang Huang ◽  
Myers Ulrich
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Carotid Artery ◽  
Blood Vessel ◽  
Nerve Cell ◽  
Clinical Application ◽  
Learning Algorithm ◽  
Linear Filter ◽  
Multi Scale ◽  
Deep Learning Algorithm

Many studies have shown that cardiovascular disease has become one of the major diseases leading to death in the world. Therefore, it is a very meaningful topic to use image segmentation technology to segment blood vessels for clinical application. In order to automatically extract the features of blood vessel images in the process of segmentation, the deep learning algorithm is combined with image segmentation technology to segment the nerve cell membrane and carotid artery images of ICU patients, and to segment the blood vessel images from a multi-dimensional perspective. The relevant data are collected to observe the effect of this model. The results show that the three-dimensional multi-scale linear filter has a good effect on carotid artery segmentation in the image segmentation of nerve cell membranes and carotid artery. When analyzing the accuracy of vascular image segmentation from network parameters and training parameters, it is found that the accuracy of the threedimensional multi-scale linear filter can reach about 85%. Therefore, it can be found that the combination of deep learning algorithm and image segmentation technology has a good segmentation effect, and the segmentation accuracy is also high. The experiment achieves the desired effect, which provides experimental basis for the clinical application of the vascular image segmentation technology.

scholarly journals Automatic and unbiased segmentation and quantification of myofibers in skeletal muscle

2021 ◽  
Author(s):  
Ariel Waisman ◽  
Alessandra Norris ◽  
Martín Elías Costa ◽  
Daniel Kopinke
Keyword(s):  
Skeletal Muscle ◽  
Primary Cilia ◽  
Learning Algorithm ◽  
Fixed Tissue ◽  
Deep Learning Algorithm ◽  
Ability To Regenerate ◽  
Murine Skeletal Muscle ◽  
And Function ◽  
Injury Models ◽  
Imagej Plugin

ABSTRACTSkeletal muscle has the remarkable ability to regenerate. However, with age and disease muscle strength and function decline. Myofiber size, which is affected by injury and disease, is a critical measurement to assess muscle health. Here, we test and apply Cellpose, a recently developed deep learning algorithm, to automatically segment myofibers within murine skeletal muscle. We first show that tissue fixation is necessary to preserve cellular structures such as primary cilia, small cellular antennae, and adipocyte lipid droplets. However, fixation generates heterogeneous myofiber labeling, which impedes intensity-based segmentation. We demonstrate that Cellpose efficiently delineates thousands of individual myofibers outlined by a variety of markers, even within fixed tissue with highly uneven myofiber staining. We created a novel ImageJ plugin (LabelsToRois) that allows processing multiple Cellpose segmentation images in batch. The plugin also contains a semi-automatic erosion function to correct for the area bias introduced by the different stainings, identifying myofibers as accurately as human experts. We successfully applied our segmentation pipeline to uncover myofiber size differences between two different muscle injury models, cardiotoxin and glycerol. Thus, Cellpose combined with LabelsToRois allows for fast, unbiased, and reproducible myofiber quantification for a variety of staining and fixation conditions.

scholarly journals Abnormal Behavior Recognition in Classroom Pose Estimation of College Students Based on Spatiotemporal Representation Learning

2021 ◽  
Vol 38 (1) ◽  
pp. 89-95
Author(s):  
Yunfang Xie ◽  
Su Zhang ◽  
Yingdi Liu
Keyword(s):  
College Students ◽  
Learning Algorithm ◽  
Three Dimensional ◽  
Representation Learning ◽  
Detection Algorithm ◽  
Abnormal Behavior ◽  
Surveillance Video ◽  
Deep Learning Algorithm ◽  
Spatiotemporal Representation ◽  
Abnormal Behaviors

Artificial intelligence and fifth generation (5G) technology are widely adopted to evaluate the classroom poses of college students, with the help of campus video surveillance equipment. To ensure the effective learning in class, it is important to detect and intervene in abnormal behaviors like sleeping and using cellphones in time. Based on spatiotemporal representation learning, this paper presents a deep learning algorithm to evaluate classroom poses of college students. Firstly, feature engineering was adopted to mine the moving trajectories of college students, which were used to determine student distribution and establish a classroom prewarning system. Then, k-means clustering (KMC) was employed for cluster analysis on different student groups, and identify the features of each group. For a specific student group, the classroom surveillance video was decomposed into several frames; the edge of each frame was extracted by edge detection algorithm, and imported to the proposed convolutional neural network (CNN). Experimental results show that our algorithm is 5% more accurate than the benchmark three-dimensional CNN (C3D), making it an effective tool to recognize abnormal behaviors of college students in class.

scholarly journals Research on Basketball Teaching Network Course Resource Recommendation Method Based on Deep Learning Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Kai Ma
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Three Dimensional ◽  
Computation Time ◽  
Personalized Learning ◽  
Learning Resource ◽  
Recognition Error ◽  
Deep Learning Algorithm ◽  
Proposed Model ◽  
Resource Data

To solve the problem of invalid resource recommendation data and poor recommendation effect in basketball teaching network course resource recommendation, a basketball teaching network course resource recommendation method based on a deep learning algorithm is proposed. The objective function is applied to eliminate the noise in the basketball teaching network course resource data. The prominent characteristics of basketball teaching network curriculum resources are extracted using a kernel function and combined into a feature set. A convolution neural network (CNN) was employed to realize the basketball teaching network curriculum resources recommendation model. The model was assessed in terms of computation time and recognition error. To validate the performance, the proposed model was compared with two well-known recommendation models such as the learning resource recommendation method based on transfer learning and the personalized learning resource recommendation method based on three-dimensional feature collaborative domination. Experimental results show that the proposed model achieved the lowest computation time of 15 s and recommendation error less than 0.4% as compared with the existing model.

scholarly journals A Non-Destructive Method for Quantifying Tissue Vascularity Using Quantitative Deep Learning Image Processing

2020 ◽  
Author(s):  
Austin Veith ◽  
Aaron B. Baker
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Ex Vivo ◽  
Imaging Techniques ◽  
Vascular Network ◽  
Three Dimensions ◽  
Surrounding Tissue ◽  
Vascular Networks ◽  
Fixed Tissue ◽  
Histological Techniques

AbstractThe quantitative analysis of blood vessel networks is an important component in the understanding and analysis of vascular disease, ischemia, cancer and many other disease states. However, many imaging techniques used for imaging vascular networks are time consuming, prevent further analyses or are technically challenging. Here, we describe a nondestructive technique for imaging the vessels in harvested tissue samples that relatively rapid and effective visualization of in situ vasculature. Furthermore, this technique allows for further analysis of the sample using histochemical staining, immunostaining and other techniques that can be performed on fixed tissue. The method uses ex vivo permeation of the tissue with an iodine solution and micro CT imaging to visualize the vascular network. Using a deep learning algorithm, we trained a convolutional neural network to automatically segment blood vessels from the surrounding tissue for analysis and create a map of the vasculature. While this method cannot achieve the resolution obtainable through destructive techniques, it is a simple and quick method for visualizing and quantifying vascular networks in three dimensions prior to analysis with conventional histological techniques. The global visualization of the three-dimensional vascular network provided by this method gives additional insights into complex changes in the vascular structure and can guide further histological analyses to specific regions of the vascular network. Overall, this method is a simple technique that can be added to most conventional tissue analyses to enhance the quantification and information derived from animal models with vascular network remodeling.

scholarly journals A Nondestructive Prescreening Method for Bone Collagen Content Using Micro-Computed Tomography

Radiocarbon ◽  
2010 ◽  
Vol 52 (2) ◽  
pp. 612-619 ◽  
Author(s):  
Jennifer A Tripp ◽  
Maria E Squire ◽  
Julie Hamilton ◽  
Robert E M Hedges
Keyword(s):  
Computed Tomography ◽  
Volume Fraction ◽  
Three Dimensional ◽  
Bone Collagen ◽  
Protein Recovery ◽  
X Rays ◽  
Micro Computed Tomography ◽  
Bone Volume Fraction ◽  
Nondestructive Technique ◽  
Low Protein

Isolation of bone collagen for radiocarbon dating is a labor-intensive and time-consuming process that sometimes results in unacceptably low protein recovery. In preliminary studies reported here, micro-computed tomography (microCT), a nondestructive technique that uses X-rays to produce high-resolution three-dimensional images of mineralized materials such as bone, offers promise as a suitable prescreening option for bones of questionable preservation. We have found that the bone volume fraction calculated by the scanner software correlates well with collagen recovery in 4 analyzed bones from Etton, United Kingdom.

scholarly journals Automatic and unbiased segmentation and quantification of myofibers in skeletal muscle

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ariel Waisman ◽  
Alessandra Marie Norris ◽  
Martín Elías Costa ◽  
Daniel Kopinke
Keyword(s):  
Skeletal Muscle ◽  
Primary Cilia ◽  
Learning Algorithm ◽  
Fixed Tissue ◽  
Deep Learning Algorithm ◽  
Ability To Regenerate ◽  
Murine Skeletal Muscle ◽  
And Function ◽  
Injury Models ◽  
Imagej Plugin

AbstractSkeletal muscle has the remarkable ability to regenerate. However, with age and disease muscle strength and function decline. Myofiber size, which is affected by injury and disease, is a critical measurement to assess muscle health. Here, we test and apply Cellpose, a recently developed deep learning algorithm, to automatically segment myofibers within murine skeletal muscle. We first show that tissue fixation is necessary to preserve cellular structures such as primary cilia, small cellular antennae, and adipocyte lipid droplets. However, fixation generates heterogeneous myofiber labeling, which impedes intensity-based segmentation. We demonstrate that Cellpose efficiently delineates thousands of individual myofibers outlined by a variety of markers, even within fixed tissue with highly uneven myofiber staining. We created a novel ImageJ plugin (LabelsToRois) that allows processing of multiple Cellpose segmentation images in batch. The plugin also contains a semi-automatic erosion function to correct for the area bias introduced by the different stainings, thereby identifying myofibers as accurately as human experts. We successfully applied our segmentation pipeline to uncover myofiber regeneration differences between two different muscle injury models, cardiotoxin and glycerol. Thus, Cellpose combined with LabelsToRois allows for fast, unbiased, and reproducible myofiber quantification for a variety of staining and fixation conditions.

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