Automated Sorting of Rare Cells Based on Autofocusing Visual Feedback in Fluorescence Microscopy
Kailun Bai, Huaping Wang, Qing Shi, Zhiqiang Zheng, Juan Cui, Tao Sun, Qiang Huang, Paolo Dario
Abstract
The research on rare cells makes a significant contribution to biology research and medical treatment for the application of diagnostic operation as well as prognoses treatment. Therefore, sorting them from heterogeneous mixtures is crucial and valuable. Traditional cell sorting methods featured with poor purity and recovery rate as well as limited flexibility, which are not ideal approaches for rare type. In this paper, we proposed a cell screening method based on automated microrobotic aspiration-and-placement strategy under fluorescence microscope. An innovative autofocusing visual feedback (AVF) method is proposed for precise three-dimensional (3D) locating of target cells. For depth detection, multiple depth from defocus (MDFD) method is adopted to solve symmetry problem and attain an average accuracy of 97.07%. For planar locating, Markov random field (MRF) based locating method is utilized to separate and locate the overlapped cells. The end actuator locating and real-time tracking are performed relying on normalized cross-correlation (NCC) method. Experiential results show that our system collects rare cells (100 cells ml-1) at a speed of 5 cells min-1 with 90% purity and 75% recovery rate, which is valuable for biological and medical application.
BibTeX
@inproceedings{iros2019_automatedsorting,
title = {Automated Sorting of Rare Cells Based on Autofocusing Visual Feedback in Fluorescence Microscopy},
author = {Kailun Bai and Huaping Wang and Qing Shi and Zhiqiang Zheng and Juan Cui and Tao Sun and Qiang Huang and Paolo Dario and Toshio Fukuda},
booktitle = {IROS 2019},
year = {2019}
}