Name
Abstract | ||
|---|---|---|
This paper proposes an automated robotic system to perform cell microinjections to relieve human operators from this highly difficult and tedious manual procedure. The system, which uses commercial equipment currently found on most biomanipulation laboratories, consists of a multitask software framework combining computer vision and robotic control elements. The vision part features an injection pipette tracker and an automatic cell targeting system that is responsible for defining injection points within the contours of adherent cells in culture. The main challenge is the use of bright-field microscopy only, without the need for chemical markers normally employed to highlight the cells. Here, cells are identified and segmented using a threshold-based image processing technique working on defocused images. Fast and precise microinjection pipette positioning over the automatically defined targets is performed by a two-stage robotic system which achieves an average injection rate of 7.6 cells/min with a pipette positioning precision of 0.23 μm. The consistency of these microinjections and the performance of the visual targeting framework were experimentally evaluated using two cell lines (CHO-K1 and HEK) and over 500 cells. In these trials, the cells were automatically targeted and injected with a fluorescent marker, resulting in a correct cell detection rate of 87% and a successful marker delivery rate of 67.5%. These results demonstrate that the new system is capable of better performances than expert operators, highlighting its benefits and potential for large-scale application. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/JBHI.2013.2248161 | Biomedical and Health Informatics, IEEE Journal of |
Keywords | Field | DocType |
adhesion,bioMEMS,cellular transport,computer vision,fluorescence,image segmentation,medical image processing,medical robotics,micromanipulators,object tracking,optical microscopy,CHO-K1,HEK,adherent cell contours,adherent cell microinjection,automated robotic system,automatic cell targeting system,average injection rate,biomanipulation laboratories,bright-field microscopy,cell identification,cell lines,cell segmentation,chemical markers,commercial equipment,computer vision,correct cell detection rate,defocused images,fluorescent marker,fully automated system,human operators,injection pipette tracker,injection points,large-scale application,manual procedure,marker delivery rate,microinjection pipette positioning,multitask software framework,pipette positioning precision,robotic control element,threshold-based image processing technique,two-stage robotic system,visual targeting framework,Cell microinjection,defocusing microscopy,robotic biomanipulation | Computer vision,Pipette,Computer science,HEK 293 cells,Microinjection,Image processing,Image segmentation,Video tracking,Artificial intelligence,Microinjections,Software framework | Journal |
Volume | Issue | ISSN |
18 | 1 | 2168-2194 |
Citations | PageRank | References |
6 | 0.89 | 12 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gabriele Becattini | 1 | 6 | 0.89 |
| Leonardo S. Mattos | 2 | 123 | 28.31 |
| Darwin G. Caldwell | 3 | 2900 | 319.72 |