A fully automated system for adherent cells microinjection.

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.

A visual targeting system for the microinjection of unstained adherent cells.

Automatic localization and targeting are critical steps in automating the process of microinjecting adherent cells. This process is currently performed manually by highly trained operators and is characterized as a laborious task with low success rate. Therefore, automation is desired to increase the efficiency and consistency of the operations. This research offers a contribution to this procedure through the development of a vision system for a robotic microinjection setup. Its goals are to automatically locate adherent cells in a culture dish and target them for a microinjection. Here the major concern was the achievement of an error-free targeting system to guarantee high consistency in microinjection experiments. To accomplish this, a novel visual targeting algorithm integrating different image processing techniques was proposed. This framework employed defocusing microscopy to highlight cell features and improve cell segmentation and targeting reliability. Three main image processing techniques, operating at three different focus levels in a bright field (BF) microscope, were used: an anisotropic contour completion (ACC) method, a local intensity variation background-foreground classifier, and a grayscale threshold-based segmentation. The proposed framework combined information gathered by each of these methods using a validation map and this was shown to provide reliable cell targeting results. Experiments conducted with sets of real images from two different cell lines (CHO-K1 and HEK), which contained a total of more than 650 cells, yielded flawless targeting results along with a cell detection ratio greater than 50%.

Comparative evaluation of user interfaces for robot-assisted laser phonomicrosurgery.

This research investigates the impact of three different control devices and two visualization methods on the precision, safety and ergonomics of a new medical robotic system prototype for assistive laser phonomicrosurgery. This system allows the user to remotely control the surgical laser beam using either a flight simulator type joystick, a joypad, or a pen display system in order to improve the traditional surgical setup composed by a mechanical micromanipulator coupled with a surgical microscope. The experimental setup and protocol followed to obtain quantitative performance data from the control devices tested are fully described here. This includes sets of path following evaluation experiments conducted with ten subjects with different skills, for a total of 700 trials. The data analysis method and experimental results are also presented, demonstrating an average 45% error reduction when using the joypad and up to 60% error reduction when using the pen display system versus the standard phonomicrosurgery setup. These results demonstrate the new system can provide important improvements in terms of surgical precision, ergonomics and safety. In addition, the evaluation method presented here is shown to support an objective selection of control devices for this application.

Diffusion tensor driven contour closing for cell microinjection targeting.

This article introduces a novel approach to robust automatic detection of unstained living cells in bright-field (BF) microscope images with the goal of producing a target list for an automated microinjection system. The overall image analysis process is described and includes: preprocessing, ridge enhancement, image segmentation, shape analysis and injection point definition. The developed algorithm implements a new version of anisotropic contour completion (ACC) based on the partial differential equation (PDE) for heat diffusion which improves the cell segmentation process by elongating the edges only along their tangent direction. The developed ACC algorithm is equivalent to a dilation of the binary edge image with a continuous elliptic structural element that takes into account local orientation of the contours preventing extension towards normal direction. Experiments carried out on real images of 10 to 50 microm CHO-K1 adherent cells show a remarkable reliability in the algorithm along with up to 85% success for cell detection and injection point definition.