scholarly journals Edge Detection of Cryptic Lamellipodia Assisted by Deep Learning

2017 ◽  
Author(s):  
Chuangqi Wang ◽  
Shawn Kang ◽  
Eunice Kim ◽  
Xitong Zhang ◽  
Hee June Choi ◽  
...  
Keyword(s):  
Neural Networks ◽  
Image Analysis ◽  
Cell Migration ◽  
Deep Learning ◽  
Computational Analysis ◽  
Time Lapse ◽  
Collective Cell Migration ◽  
Edge Detector ◽  
Cell Protrusion ◽  
Canny Edge

AbstractCell protrusion plays important roles in cell migration by pushing plasma membrane forward. Cryptic lamellipodia induce the protrusion of submarginal cells in collective cell migration where cells are attached and move together. Although computational image analysis of cell protrusion has been done extensively, the study on protrusion activities of cryptic lamellipodia is limited due to difficulties in image segmentation. This study seeks to aid in the computational analysis of submarginal cell protrusion in collective cell migration by using deep learning to detect the cryptic lamellipodial edges from fluorescence time-lapse movies. Due to the noisy features within overlapping cells, the conventional image analysis algorithms such as Canny edge detector and intensity thresholding are limited. In this paper we combined Canny edge detector, Deep Neural Networks (DNNs), and local intensity thresholding. We were able to detect cryptic lamellipodial edges of submarginal cells with high accuracy from the fluorescence time-lapse movies of PtK1 cells using both simple convolutional neural networks and VGG-16 based neural networks. We used relatively small effort to prepare the training set to train the DNNs to detect the cryptical lamellipodial edges in fluorescence time-lapse movies. This work demonstrates that deep learning can be combined with the conventional image analysis algorithms to facilitate the computational analysis of highly complex time-lapse movies of collective cell migration.

scholarly journals Image Analysis using Deep Learning Techniques

2019 ◽  
Vol 8 (6S3) ◽  
pp. 1385-1388
Keyword(s):  
Neural Networks ◽  
Image Analysis ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Learning Techniques ◽  
Massive Information ◽  
Comparative Examination ◽  
Hypothetical Structure ◽  
The Way

In late years, critical learning methodologies especially Convolutional Neural Networks have been utilized in different solicitations. CNN's have appeared to be a key capacity to ordinarily expel broad volumes of data from massive information. The uses of CNNs have inside and out ended up being useful especially in orchestrating ordinary pictures. Regardless, there have been essential obstacles in executing the CNNs in a restorative zone as a result of the nonattendance of genuine getting ready data. Consequently, general imaging benchmarks, for instance, Image Net have been conspicuously used in the restorative not too zone notwithstanding the way that they are perfect when appeared differently about the CNNs. In this paper, a comparative examination of LeNet, AlexNet, and GoogLeNet has been done. Starting there, the paper has proposed an improved hypothetical structure for requesting helpful life structures pictures using CNNs. In perspective on the proposed structure of the framework, the CNNs building are required to beat the previous three plans in requesting remedial pictures.

scholarly journals A Step-by-Step Guide for Automated Plant Canopy Delineation Using Deep Learning: An Example in Strawberry Using ArcGIS Pro Software

EDIS ◽  
2021 ◽  
Vol 2021 (5) ◽  
Author(s):  
Amr Abd-Elrahman ◽  
Katie Britt ◽  
Vance Whitaker
Keyword(s):  
Neural Networks ◽  
Information System ◽  
Image Analysis ◽  
Deep Learning ◽  
Geographic Information System ◽  
Precision Agriculture ◽  
System Analysis ◽  
Plant Canopy ◽  
Geographic Information System Analysis ◽  
Agriculture Management

This publication presents a guide to image analysis for researchers and farm managers who use ArcGIS software. Anyone with basic geographic information system analysis skills may follow along with the demonstration and learn to implement the Mask Region Convolutional Neural Networks model, a widely used model for object detection, to delineate strawberry canopies using ArcGIS Pro Image Analyst Extension in a simple workflow. This process is useful for precision agriculture management.

scholarly journals Training a neural network to learn other dimensionality reduction removes data size restrictions in bioinformatics and provides a new route to exploring data representations

2020 ◽  
Author(s):  
Alex Dexter ◽  
Spencer A. Thomas ◽  
Rory T. Steven ◽  
Kenneth N. Robinson ◽  
Adam J. Taylor ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Dimensionality Reduction ◽  
Computational Analysis ◽  
New Technologies ◽  
State Of The Art ◽  
Current State ◽  
Data Representations ◽  
Non Linear ◽  
Linear Dimensionality Reduction

AbstractHigh dimensionality omics and hyperspectral imaging datasets present difficult challenges for feature extraction and data mining due to huge numbers of features that cannot be simultaneously examined. The sample numbers and variables of these methods are constantly growing as new technologies are developed, and computational analysis needs to evolve to keep up with growing demand. Current state of the art algorithms can handle some routine datasets but struggle when datasets grow above a certain size. We present a training deep learning via neural networks on non-linear dimensionality reduction, in particular t-distributed stochastic neighbour embedding (t-SNE), to overcome prior limitations of these methods.One Sentence SummaryAnalysis of prohibitively large datasets by combining deep learning via neural networks with non-linear dimensionality reduction.

scholarly journals Deep Learning-based Trichoscopic Image Analysis and Quantitative Model for Predicting Basic and Specific Classification in Male Androgenic Alopecia

2021 ◽  
Author(s):  
Meng Gao ◽  
Yue Wang ◽  
Haipeng Xu ◽  
Congcong Xu ◽  
Xianhong Yang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Image Analysis ◽  
Deep Learning ◽  
Quantitative Model ◽  
High Accuracy ◽  
Diameter Distribution ◽  
Androgenic Alopecia ◽  
Hair Density ◽  
Learning Framework ◽  
Quantitative Indicators

Since the results of basic and specific classification in male androgenic alopecia are subjective, and trichoscopic data, such as hair density and diameter distribution, are potential quantitative indicators, the aim of this study was to develop a deep learning framework for automatic trichoscopic image analysis and a quantitative model for predicting basic and specific classification in male androgenic alopecia. A total of 2,910 trichoscopic images were collected and a deep learning framework was created on convolutional neural networks. Based on the trichoscopic data provided by the framework, correlations with basic and specific classification were analysed and a quantitative model was developed for predicting basic and specific classification using multiple ordinal logistic regression. The aim of this study was to develop a deep learning framework that can accurately analyse hair density and diameter distribution on trichoscopic images, and a quantitative model for predicting basic and specific classification in male androgenic alopecia with high accuracy.

scholarly journals Using deep reinforcement learning to speed up collective cell migration

2019 ◽  
Vol 20 (S18) ◽  
Author(s):  
Hanxu Hou ◽  
Tian Gan ◽  
Yaodong Yang ◽  
Xianglei Zhu ◽  
Sen Liu ◽  
...  
Keyword(s):  
Cell Migration ◽  
Reinforcement Learning ◽  
Real Life ◽  
Time Lapse ◽  
Research Process ◽  
Agent Based Modeling ◽  
Collective Cell Migration ◽  
Modeling Framework ◽  
Agent Based ◽  
Number Of Cells

Abstract Background Collective cell migration is a significant and complex phenomenon that affects many basic biological processes. The coordination between leader cell and follower cell affects the rate of collective cell migration. However, there are still very few papers on the impacts of the stimulus signal released by the leader on the follower. Tracking cell movement using 3D time-lapse microscopy images provides an unprecedented opportunity to systematically study and analyze collective cell migration. Results Recently, deep reinforcement learning algorithms have become very popular. In our paper, we also use this method to train the number of cells and control signals. By experimenting with single-follower cell and multi-follower cells, it is concluded that the number of stimulation signals is proportional to the rate of collective movement of the cells. Such research provides a more diverse approach and approach to studying biological problems. Conclusion Traditional research methods are always based on real-life scenarios, but as the number of cells grows exponentially, the research process is too time consuming. Agent-based modeling is a robust framework that approximates cells to isotropic, elastic, and sticky objects. In this paper, an agent-based modeling framework is used to establish a simulation platform for simulating collective cell migration. The goal of the platform is to build a biomimetic environment to demonstrate the importance of stimuli between the leading and following cells.

scholarly journals Tissue self-organization based on collective cell migration by contact activation of locomotion and chemotaxis

2019 ◽  
Vol 116 (10) ◽  
pp. 4291-4296 ◽  
Author(s):  
Taihei Fujimori ◽  
Akihiko Nakajima ◽  
Nao Shimada ◽  
Satoshi Sawai
Keyword(s):  
Cell Migration ◽  
Cell Movement ◽  
Tissue Level ◽  
Cell Contact ◽  
Collective Cell Migration ◽  
Contact Activation ◽  
Membrane Recruitment ◽  
Cell Rearrangement ◽  
Cell Protrusion ◽  
Cell Cell

Despite their central role in multicellular organization, navigation rules that dictate cell rearrangement remain largely undefined. Contact between neighboring cells and diffusive attractant molecules are two of the major determinants of tissue-level patterning; however, in most cases, molecular and developmental complexity hinders one from decoding the exact governing rules of individual cell movement. A primordial example of tissue patterning by cell rearrangement is found in the social amoebaDictyostelium discoideumwhere the organizing center or the “tip” self-organizes as a result of sorting of differentiating prestalk and prespore cells. By employing microfluidics and microsphere-based manipulation of navigational cues at the single-cell level, here we uncovered a previously overlooked mode ofDictyosteliumcell migration that is strictly directed by cell–cell contact. The cell–cell contact signal is mediated by E-set Ig-like domain-containing heterophilic adhesion molecules TgrB1/TgrC1 that act in trans to induce plasma membrane recruitment of the SCAR complex and formation of dendritic actin networks, and the resulting cell protrusion competes with those induced by chemoattractant cAMP. Furthermore, we demonstrate that both prestalk and prespore cells can protrude toward the contact signal as well as to chemotax toward cAMP; however, when given both signals, prestalk cells orient toward the chemoattractant, whereas prespore cells choose the contact signal. These data suggest a model of cell sorting by competing juxtacrine and diffusive cues, each with potential to drive its own mode of collective cell migration.

Developing Deep-Learning Methods for Diagnosis and Prognosis of Pediatric Progressive Diseases Using Modern Imaging Techniques

2021 ◽  
Author(s):  
◽  
Mahdieh Shabanian ◽  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Image Analysis ◽  
Deep Learning ◽  
Neurological Disease ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Disease State ◽  
Institutional Review Board ◽  
Institutional Review

Purpose and Rationale. Central nervous system manifestations form a significant burden of disease in young children. There have been efforts to correlate the neurological disease state in tuberous sclerosis complex (TSC) neurological disease state with imaging findings is a standard part of patient care. However, such analysis of neuroimaging is time- and labor-intensive. Automated approaches to these tasks are needed to improve speed, accuracy, and availability. Automated medical image analysis tools based on 3D/2D deep learning algorithms can help improve the quality and consistency of image diagnosis and interpretation for cognitive disorders in infants. We propose to automate neuroimaging analysis with artificial intelligence algorithms. This novel approach can be used to improve the accuracy of TSC diagnosis and treatment. Deep learning (DL) is among the most successful types of machine learning and utilizes deep artificial neural networks (ANNs), which can determine efficient feature representations of input data. DL algorithms have created new opportunities in medical image analysis. Applications of DL, specifically convolutional neural networks (CNNs), in medical image analysis, cover a broad spectrum of tasks, including risk prediction/estimation with a machine learning system trained on these classification tasks. Study population. We reviewed an NIMH Data Archive (NDA) dataset that was collected in 2010. We also reviewed imaging data from patients and normal cases from birth to 8 years of age acquired at Le Bonheur Children’s Hospital from 2014 to 2020. The University of Tennessee Health Science Center Institutional Review Board (IRB) approved this study. Research Design and Study Procedures. Following Institutional Review Board (IRB) approval, this thesis: 1) Presents the first 2D/3D fusion CNN models to estimate the age of infants from birth to 3 years of age. 2) Presents the first work to look at whole-brain network to automatically distinguish TSC brain structural pathology from normal cases using a 3DCNN model. Conclusions. The study findings indicate that deep neural networks tackle the problem of early prediction of cognitive and neurodevelopmental disorders and structural brain pathology based on MRI automatically in TSC children. It is the hope of the author that analysis of MRI images via methods of deep learning will have a positive impact on healthcare for infants and children at risk of rare diseases.

scholarly journals Collective migration during a gap closure in a two-dimensional haptotactic model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Marie Versaevel ◽  
Laura Alaimo ◽  
Valentine Seveau ◽  
Marine Luciano ◽  
Danahe Mohammed ◽  
...  
Keyword(s):  
Cell Migration ◽  
Cell Density ◽  
Healing Process ◽  
Time Lapse ◽  
Wound Healing Process ◽  
Collective Cell Migration ◽  
Two Dimensional ◽  
Gap Closure ◽  
Lower Cell Density ◽  
Protein Gradients

AbstractThe ability of cells to respond to substrate-bound protein gradients is crucial for many physiological processes, such as immune response, neurogenesis and cancer cell migration. However, the difficulty to produce well-controlled protein gradients has long been a limitation to our understanding of collective cell migration in response to haptotaxis. Here we use a photopatterning technique to create circular, square and linear fibronectin (FN) gradients on two-dimensional (2D) culture substrates. We observed that epithelial cells spread preferentially on zones of higher FN density, creating rounded or elongated gaps within epithelial tissues over circular or linear FN gradients, respectively. Using time-lapse experiments, we demonstrated that the gap closure mechanism in a 2D haptotaxis model requires a significant increase of the leader cell area. In addition, we found that gap closures are slower on decreasing FN densities than on homogenous FN-coated substrate and that fresh closed gaps are characterized by a lower cell density. Interestingly, our results showed that cell proliferation increases in the closed gap region after maturation to restore the cell density, but that cell–cell adhesive junctions remain weaker in scarred epithelial zones. Taken together, our findings provide a better understanding of the wound healing process over protein gradients, which are reminiscent of haptotaxis.

scholarly journals The Zombification of Art History

2019 ◽  
Vol 11 (2) ◽  
pp. 28-35
Author(s):  
Tsila Hassine ◽  
Ziv Neeman
Keyword(s):  
Neural Networks ◽  
Image Analysis ◽  
Deep Learning ◽  
Art History ◽  
The Past ◽  
New Directions ◽  
Artistic Image

In the past few years deep-learning AI neural networks have achieved major milestones in artistic image analysis and generation, producing what some refer to as ‘art.’ We reflect critically on some of the artistic shortcomings of a few projects that occupied the spotlight in recent years. We introduce the term ‘Zombie Art’ to describe the generation of new images of dead masters, as well as ‘The AI Reproducibility Test.’ We designate the problems inherent in AI and in its application to art history. In conclusion, we propose new directions for both AI-generated art and art history, in the light of these new powerful AI technologies of artistic image analysis and generation.

Sign in / Sign up

Export Citation Format

Share Document