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.

scholarly journals DALMATIAN: An Algorithm for Automatic Cell Detection and Counting in 3D

2017 ◽  
Vol 11 ◽  
Author(s):  
Sergey A. Shuvaev ◽  
Alexander A. Lazutkin ◽  
Alexander V. Kedrov ◽  
Konstantin V. Anokhin ◽  
Grigori N. Enikolopov ◽  
...  
Keyword(s):  
Cell Detection ◽  
Detection And Counting

scholarly journals Hybrid microchannel-solid state micropore device for fast and optical cell detection

RSC Advances ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 5361-5370
Author(s):  
Carol M. Olmos ◽  
Gustavo Rosero ◽  
Tamara Fernández-Cabada ◽  
Ross Booth ◽  
Manuel Der ◽  
...  
Keyword(s):  
Solid State ◽  
Cell Detection ◽  
Optical Cell ◽  
Detection And Counting

This paper presents a methodology for cell detection and counting using a device that combines PDMS (polydimethylsiloxane) microfluidic multilayer channels with a single solid state micropore.

scholarly journals ACDC: Automated Cell Detection and Counting for Time-Lapse Fluorescence Microscopy

2020 ◽  
Vol 10 (18) ◽  
pp. 6187
Author(s):  
Leonardo Rundo ◽  
Andrea Tangherloni ◽  
Darren R. Tyson ◽  
Riccardo Betta ◽  
Carmelo Militello ◽  
...  
Keyword(s):  
Large Scale ◽  
Time Lapse ◽  
Cell Counting ◽  
Cell Detection ◽  
Cell Nuclei ◽  
Similarity Coefficients ◽  
Laboratory Practice ◽  
Microscopy Imaging ◽  
Time Lapse Microscopy ◽  
Detection And Counting

Advances in microscopy imaging technologies have enabled the visualization of live-cell dynamic processes using time-lapse microscopy imaging. However, modern methods exhibit several limitations related to the training phases and to time constraints, hindering their application in the laboratory practice. In this work, we present a novel method, named Automated Cell Detection and Counting (ACDC), designed for activity detection of fluorescent labeled cell nuclei in time-lapse microscopy. ACDC overcomes the limitations of the literature methods, by first applying bilateral filtering on the original image to smooth the input cell images while preserving edge sharpness, and then by exploiting the watershed transform and morphological filtering. Moreover, ACDC represents a feasible solution for the laboratory practice, as it can leverage multi-core architectures in computer clusters to efficiently handle large-scale imaging datasets. Indeed, our Parent-Workers implementation of ACDC allows to obtain up to a 3.7× speed-up compared to the sequential counterpart. ACDC was tested on two distinct cell imaging datasets to assess its accuracy and effectiveness on images with different characteristics. We achieved an accurate cell-count and nuclei segmentation without relying on large-scale annotated datasets, a result confirmed by the average Dice Similarity Coefficients of 76.84 and 88.64 and the Pearson coefficients of 0.99 and 0.96, calculated against the manual cell counting, on the two tested datasets.

scholarly journals ACDC: Automated Cell Detection and Counting for Time-lapse Fluorescence Microscopy

2020 ◽  
Author(s):  
Leonardo Rundo ◽  
Andrea Tangherloni ◽  
Darren R. Tyson ◽  
Riccardo Betta ◽  
Carmelo Militello ◽  
...  
Keyword(s):  
Large Scale ◽  
Time Lapse ◽  
Cell Counting ◽  
Cell Detection ◽  
Cell Nuclei ◽  
Similarity Coefficients ◽  
Laboratory Practice ◽  
Microscopy Imaging ◽  
Time Lapse Microscopy ◽  
Detection And Counting

AbstractAdvances in microscopy imaging technologies have enabled the visualization of live-cell dynamic processes using time-lapse microscopy imaging. However, modern methods exhibit several limitations related to the training phases and to time constraints, hindering their application in the laboratory practice. In this work, we present a novel method, named Automated Cell Detection and Counting (ACDC), designed for activity detection of fluorescent labeled cell nuclei in time-lapse microscopy. ACDC overcomes the limitations of the literature methods, by first applying bilateral filtering on the original image to smooth the input cell images while preserving edge sharpness, and then by exploiting the watershed transform and morphological filtering. Moreover, ACDC represents a feasible solution for the laboratory practice, as it can leverage multi-core architectures in computer clusters to efficiently handle large-scale imaging datasets. Indeed, our Parent-Workers implementation of ACDC allows to obtain up to a 3.7× speed-up compared to the sequential counterpart. ACDC was tested on two distinct cell imaging datasets to assess its accuracy and effectiveness on images with different characteristics. We achieved an accurate cell-count and nuclei segmentation without relying on large-scale annotated datasets, a result confirmed by the average Dice Similarity Coefficients of 76.84 and 88.64 and the Pearson coefficients of 0.99 and 0.96, calculated against the manual cell counting, on the two tested datasets.

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