scholarly journals Deep learning optical-sectioning method

2018 ◽  
Vol 26 (23) ◽  
pp. 30762 ◽  
Author(s):  
Xiaoyu Zhang ◽  
Yifan Chen ◽  
Kefu Ning ◽  
Can Zhou ◽  
Yutong Han ◽  
...  
Keyword(s):  
Deep Learning ◽  
Optical Sectioning ◽  
Sectioning Method

scholarly journals An Optical Sectioning Method for 3D Reconstruction Using 4D-STEM

2020 ◽  
Vol 26 (S2) ◽  
pp. 2454-2455
Author(s):  
Hamish Brown ◽  
Phillipp Pelz ◽  
Shang-Lin Hsu ◽  
Ramamoorthy Ramesh ◽  
Mary Scott ◽  
...  
Keyword(s):  
3D Reconstruction ◽  
Optical Sectioning ◽  
Sectioning Method

Application of Confocal Laser Scanning Microscopy in Bone

1998 ◽  
Vol 02 (01) ◽  
pp. 65-71 ◽  
Author(s):  
H. Fujii ◽  
D. J. Wood ◽  
J. M. Papadimitriou ◽  
M. H. Zheng
Keyword(s):  
Confocal Laser Scanning Microscopy ◽  
Laser Scanning ◽  
Bone Allograft ◽  
Laser Scanning Microscopy ◽  
Confocal Laser Scanning ◽  
Scanning Microscopy ◽  
Confocal Laser ◽  
Optical Sectioning ◽  
Sectioning Method ◽  
Plastic Embedding

The optical sectioning method of confocal laser scanning microscopy provides higher resolution than standard light microscope techniques. The use of optical rather than physical sections for detailed histological analyses of bone obviates the need for either decalcification or complex plastic embedding processes which are required as a routine for the preparation of thin microtome sections. In this study we have used confocal laser scanning microscopy for the morphological analyses of fresh unembedding human cortical bone, bone allograft and bone cement interfaces. Our results have indicated that such an approach has provided a relatively easy and rapid means for the assessment of the histology of normal and pathological bone.

scholarly journals Programmable Array Microscopes

2001 ◽  
Vol 9 (2) ◽  
pp. 8-13
Author(s):  
Quentin Hanley ◽  
Rainer Heintzmann ◽  
Donna Arndt-Jovin ◽  
Thomas Jovin
Keyword(s):  
Spatial Light Modulator ◽  
Image Plane ◽  
Regions Of Interest ◽  
Optical Sectioning ◽  
Light Modulator ◽  
Spatial Encoding ◽  
Sectioning Method

The programmable array microscope (PAM) is a powerful tool combining the capabilities of nearly all previously described optical sectioning techniques in a single microscope. Not only can the user create optical sections of threedimensional objects, but the PAM's unique adaptive optical strategy allows a user to select the best sectioning method for a particular sample or experimental need. The key to the PAM is a spatial light modulator (SLM). This device, when placed in the image plane of a microscope, can be used to create optical sectioning, generate spatial encoding masks, and/or define regions of interest.

Deposited emulsion particles in the pores of aluminum oxide film

1978 ◽  
Vol 36 (1) ◽  
pp. 450-451
Author(s):  
Michio Ashida ◽  
Yasukiyo Ueda
Keyword(s):  
Oxide Film ◽  
Acid Solution ◽  
Barrier Layer ◽  
Colloidal Particles ◽  
Oxalic Acid Solution ◽  
Anodic Oxide ◽  
Anodic Oxide Film ◽  
Carbon Replica ◽  
Sectioning Method ◽  
Thin Sectioning

An anodic oxide film is formed on aluminum in an acidic elecrolyte during anodizing. The structure of the oxide film was observed directly by carbon replica method(l) and ultra-thin sectioning method(2). The oxide film consists of barrier layer and porous layer constructed with fine hexagonal cellular structure. The diameter of micro pores and the thickness of barrier layer depend on the applying voltage and electrolyte. Because the dimension of the pore corresponds to that of colloidal particles, many metals deposit in the pores. When the oxide film is treated as anode in emulsion of polyelectrolyte, the emulsion particles migrate onto the film and deposit on it. We investigated the behavior of the emulsion particles during electrodeposition.Aluminum foils (99.3%) were anodized in either 0.25M oxalic acid solution at 30°C or 3M sulfuric acid solution at 20°C. After washing with distilled water, the oxide films used as anode were coated with emulsion particles by applying voltage of 200V and then they were cured at 190°C for 30 minutes.

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.

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