Blood cell detection and counting in holographic lens-free imaging by convolutional sparse dictionary learning and coding

2017 ◽  
Author(s):  
Florence Yellin ◽  
Benjamin D. Haeffele ◽  
Rene Vidal
Keyword(s):  
Blood Cell ◽  
Dictionary Learning ◽  
Cell Detection ◽  
Sparse Dictionary Learning ◽  
Detection And Counting ◽  
Holographic Lens

scholarly journals Automated Blood Cell Detection and Counting via Deep Learning for Microfluidic Point-of-Care Medical Devices

2019 ◽  
Vol 646 ◽  
pp. 012048 ◽  
Author(s):  
Tiancheng Xia ◽  
Richard Jiang ◽  
Yong Qing Fu ◽  
Nanlin Jin
Keyword(s):  
Deep Learning ◽  
Blood Cell ◽  
Medical Devices ◽  
Point Of Care ◽  
Cell Detection ◽  
Detection And Counting

Sparse Dictionary Learning for Blind Hyperspectral Unmixing

2019 ◽  
Vol 16 (4) ◽  
pp. 578-582 ◽  
Author(s):  
Yang Liu ◽  
Yi Guo ◽  
Feng Li ◽  
Lei Xin ◽  
Puming Huang
Keyword(s):  
Dictionary Learning ◽  
Hyperspectral Unmixing ◽  
Sparse Dictionary Learning

scholarly journals SPICE: Superpixel Classification for Cell Detection and Counting

2018 ◽  
Author(s):  
Oman Magaña-Tellez ◽  
Michalis Vrigkas ◽  
Christophoros Nikou ◽  
Ioannis Kakadiaris
Keyword(s):  
Cell Detection ◽  
Detection And Counting

Methodological Implications on Quantitative Studies of Cytocompatibility in Direct Contact with Bioceramic Surfaces

2011 ◽  
Vol 493-494 ◽  
pp. 325-330 ◽  
Author(s):  
J.A. Cortês ◽  
Elena Mavropoulos ◽  
Moema Hausen ◽  
Alexandre Rossi ◽  
J.M. Granjeiro ◽  
...  
Keyword(s):  
Cell Adhesion ◽  
Crystal Violet ◽  
Fluorescent Dyes ◽  
Ceramic Materials ◽  
Neutral Red ◽  
Cell Counting ◽  
Cell Detection ◽  
Porous Hydroxyapatite ◽  
Detection And Counting ◽  
Direct Cell

Cell adhesion, proliferation and differentiation are important specific parameters to be evaluated on biocompatibility studies of candidate biomaterials for clinical applications. Several different methodologies have been employed to study, both qualitative and quantitatively, the direct interactions of ceramic materials with cultured mammal and human cells. However, while quantitatively evaluating cell density, viability and metabolic responses to test materials, several methodological challenges may arise, either by impairing the use of some widely applied techniques, or by generating false or conflicting results. In this work, we tested the inherent interference of different representative calcium phosphate ceramic surfaces (stoichiometric dense and porous hydroxyapatite (HA) and cation-substituted apatite tablets) on different tests for quantitative evaluation of osteoblast adhesion and metabolism, either based on direct cell counting after trypsinization, colorimetric assays (XTT, Neutral Red and Crystal Violet) and fluorescence microscopy. Cell adhesion estimation after trypsinization was highly dependent on the time of treatment, and the group with the highest level of estimated adhesion was inverted from 5 to 20 minutes of exposition to trypsin. Both dense and porous HA samples presented high levels of background adsorption of the Crystal Violet dye, impairing cell detection. HA surfaces also were able to adsorb high levels of fluorescent dyes (DAPI and phalloidin-TRITC), generating backgrounds which, in the case of porous HA, impaired cell detection and counting by image processing software (Image Pro Plus 6.0). We conclude that the choice for the most suitable method for cell detection and estimation is highly dependent on very specific characteristics of the studied material, and methodological adaptations on well established protocols must always be carefully taken on consideration.

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