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.
Three-dimensional autofocusing visual feedback for automated rare cells sorting in fluorescence microscopy
Dario P.;
2019-01-01
Abstract
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
