Next Article in Journal
Using pH-Activable Carbon Nanoparticles as Cell Imaging Probes
Next Article in Special Issue
Tele–Robotic Platform for Dexterous Optical Single-Cell Manipulation
Previous Article in Journal
Experimental Microwave Complex Conductivity Extraction of Vertically Aligned MWCNT Bundles for Microwave Subwavelength Antenna Design
Previous Article in Special Issue
A Contactless and Biocompatible Approach for 3D Active Microrobotic Targeted Drug Delivery
Open AccessArticle

Three-Dimensional Autofocusing Visual Feedback for Automated Rare Cells Sorting in Fluorescence Microscopy

1
Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology, Beijing 100081, China
2
Intelligent Robotics Institute, School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China
3
Key Laboratory of Biomimetic Robots and Systems (Beijing Institute of Technology), Ministry of Education, Beijing 100081, China
4
Biorobotics Institute, Scuola Superiore Sant’Anna, Viale Rinaldo Piaggio 34, 56025 Pontedera, Pisa, Italy
*
Author to whom correspondence should be addressed.
Micromachines 2019, 10(9), 567; https://doi.org/10.3390/mi10090567
Received: 22 July 2019 / Revised: 13 August 2019 / Accepted: 26 August 2019 / Published: 27 August 2019
(This article belongs to the Special Issue Robotic Micromanipulation)
Sorting rare cells from heterogeneous mixtures makes a significant contribution to biological research and medical treatment. However, the performances of traditional methods are limited due to the time-consuming preparation, poor purity, and recovery rate. In this paper, we proposed a cell screening method based on the automated microrobotic aspirate-and-place strategy under fluorescence microscopy. A fast autofocusing visual feedback (FAVF) method is introduced for precise and real-time three-dimensional (3D) location. In the context of this method, the scalable correlation coefficient (SCC) matching is presented for planar locating cells with regions of interest (ROI) created for autofocusing. When the overlap occurs, target cells are separated by a segmentation algorithm. To meet the shallow depth of field (DOF) limitation of the microscope, the improved multiple depth from defocus (MDFD) algorithm is used for depth detection, taking 850 ms a time with an accuracy rate of 96.79%. The neighborhood search based algorithm is applied for the tracking of the micropipette. Finally, experiments of screening NIH/3T3 (mouse embryonic fibroblast) cells verifies the feasibility and validity of this method with an average speed of 5 cells/min, 95% purity, and 80% recovery rate. Moreover, such versatile functions as cell counting and injection, for example, could be achieved by this expandable system. View Full-Text
Keywords: micromanipulation; visual feedback; autofocusing; rare cells sorting; fluorescence microscopy micromanipulation; visual feedback; autofocusing; rare cells sorting; fluorescence microscopy
Show Figures

Figure 1

MDPI and ACS Style

Wang, H.; Bai, K.; Cui, J.; Shi, Q.; Sun, T.; Huang, Q.; Dario, P.; Fukuda, T. Three-Dimensional Autofocusing Visual Feedback for Automated Rare Cells Sorting in Fluorescence Microscopy. Micromachines 2019, 10, 567.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop