scholarly journals Neuron Tracing- and Deep Learning-guided Interactive Proofreading for Neuron Structure Segmentation

2021 ◽  
Vol 27 (4) ◽  
pp. 1-9
Author(s):  
JunYoung Choi ◽  
Won-Ki Jeong
Keyword(s):  
Deep Learning ◽  
Neuron Tracing

scholarly journals DeepNeuron: an open deep learning toolbox for neuron tracing

2018 ◽  
Vol 5 (2) ◽  
Author(s):  
Zhi Zhou ◽  
Hsien-Chi Kuo ◽  
Hanchuan Peng ◽  
Fuhui Long
Keyword(s):  
Deep Learning ◽  
Neuron Tracing

scholarly journals DeepNeuron: An Open Deep Learning Toolbox for Neuron Tracing

2018 ◽  
Author(s):  
Zhi Zhou ◽  
Hsien-Chi Kuo ◽  
Hanchuan Peng ◽  
Fuhui Long
Keyword(s):  
Deep Learning ◽  
Light Microscopy ◽  
Three Dimensional ◽  
Brain Structures ◽  
Learning Networks ◽  
Neuron Morphology ◽  
Neuron Tracing ◽  
Wide Range ◽  
Confocal Images ◽  
Microscopy Images

AbstractReconstructing three-dimensional (3D) morphology of neurons is essential to understanding brain structures and functions. Over the past decades, a number of neuron tracing tools including manual, semi-automatic, and fully automatic approaches have been developed to extract and analyze 3D neuronal structures. Nevertheless, most of them were developed based on coding certain rules to extract and connect structural components of a neuron, showing limited performance on complicated neuron morphology. Recently, deep learning outperforms many other machine learning methods in a wide range of image analysis and computer vision tasks. Here we developed a new open source toolbox, DeepNeuron, which uses deep learning networks to learn features and rules from data and trace neuron morphology in light microscopy images. DeepNeuron provides a family of modules to solve basic yet challenging problems in neuron tracing. These problems include but not limited to: (1) detecting neuron signal under different image conditions, (2) connecting neuronal signals into tree(s), (3) pruning and refining tree morphology, (4) quantifying the quality of morphology, and (5) classifying dendrites and axons in real time. We have tested DeepNeuron using light microscopy images including bright-field and confocal images of human and mouse brain, on which DeepNeuron demonstrates robustness and accuracy in neuron tracing.

scholarly journals Neuron tracing and quantitative analyses of dendritic architecture reveal symmetrical three-way-junctions and phenotypes of git-1 in C. elegans

2021 ◽  
Vol 17 (7) ◽  
pp. e1009185
Author(s):  
Omer Yuval ◽  
Yael Iosilevskii ◽  
Anna Meledin ◽  
Benjamin Podbilewicz ◽  
Tom Shemesh
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Dendritic Morphology ◽  
Control Mechanisms ◽  
Structural Elements ◽  
Molecular Control ◽  
Dendritic Trees ◽  
C Elegans ◽  
Neuron Tracing ◽  
Quantitative Analyses

Complex dendritic trees are a distinctive feature of neurons. Alterations to dendritic morphology are associated with developmental, behavioral and neurodegenerative changes. The highly-arborized PVD neuron of C. elegans serves as a model to study dendritic patterning; however, quantitative, objective and automated analyses of PVD morphology are missing. Here, we present a method for neuronal feature extraction, based on deep-learning and fitting algorithms. The extracted neuronal architecture is represented by a database of structural elements for abstracted analysis. We obtain excellent automatic tracing of PVD trees and uncover that dendritic junctions are unevenly distributed. Surprisingly, these junctions are three-way-symmetrical on average, while dendritic processes are arranged orthogonally. We quantify the effect of mutation in git-1, a regulator of dendritic spine formation, on PVD morphology and discover a localized reduction in junctions. Our findings shed new light on PVD architecture, demonstrating the effectiveness of our objective analyses of dendritic morphology and suggest molecular control mechanisms.

Deep Learning

2009 ◽  
Author(s):  
Stellan Ohlsson
Keyword(s):  
Deep Learning

Intelligence artificielle : le futur de l’Orthodontie ?

2019 ◽  
Vol 53 (3) ◽  
pp. 281-294
Author(s):  
Jean-Michel Foucart ◽  
Augustin Chavanne ◽  
Jérôme Bourriau
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Intelligence Artificielle ◽  
Set Up

Nombreux sont les apports envisagés de l’Intelligence Artificielle (IA) en médecine. En orthodontie, plusieurs solutions automatisées sont disponibles depuis quelques années en imagerie par rayons X (analyse céphalométrique automatisée, analyse automatisée des voies aériennes) ou depuis quelques mois (analyse automatique des modèles numériques, set-up automatisé; CS Model +, Carestream Dental™). L’objectif de cette étude, en deux parties, est d’évaluer la fiabilité de l’analyse automatisée des modèles tant au niveau de leur numérisation que de leur segmentation. La comparaison des résultats d’analyse des modèles obtenus automatiquement et par l’intermédiaire de plusieurs orthodontistes démontre la fiabilité de l’analyse automatique; l’erreur de mesure oscillant, in fine, entre 0,08 et 1,04 mm, ce qui est non significatif et comparable avec les erreurs de mesures inter-observateurs rapportées dans la littérature. Ces résultats ouvrent ainsi de nouvelles perspectives quand à l’apport de l’IA en Orthodontie qui, basée sur le deep learning et le big data, devrait permettre, à moyen terme, d’évoluer vers une orthodontie plus préventive et plus prédictive.

Vollautomatische Quantifizierung der Volumina und Funktion des linken Ventrikels in der kardialen MRT: Evaluation eines Deep-Learning basierten Algorithmus

2020 ◽  
Author(s):  
B Böttcher ◽  
E Beller ◽  
A Busse ◽  
F Streckenbach ◽  
M Weber ◽  
...  
Keyword(s):  
Deep Learning

Primäre ZNS-Lymphome: Vollautomatisierte Detektion und Segmentierung auf multiparametrischen MRT-Datensätzen mittels Deep-Learning

2020 ◽  
Author(s):  
L Pennig ◽  
L Lourenco Caldeira ◽  
C Hoyer ◽  
L Görtz ◽  
R Shahzad ◽  
...  
Keyword(s):  
Deep Learning

Computertomografie: Bildrekonstruktion mit Deep Learning in der klinischen Routine

2020 ◽  
Author(s):  
A Heinrich ◽  
M Engler ◽  
D Dachoua ◽  
U Teichgräber ◽  
F Güttler
Keyword(s):  
Deep Learning
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