Research on three-dimensional counting algorithm of high-precision suspended cells based on deep learning

Abstract

The research on the three-dimensional counting algorithm of suspension cells is very important for the online counting of cells. Suspension cells are usually observed and counted by taking out cell samples under the microscope. This method cannot realize the online counting of cells. At the same time, there will be weak movement of suspended cells in three-dimensional space, making space counting more difficult. Aiming at the above difficulties, a three-dimensional counting algorithm of suspended cells based on deep learning is proposed. The Faster regions with convolutional neural network (R-CNN) framework is used to identify the target cells, the feature pyramid structure is introduced to reduce the feature loss in the fusion process, and the soft non-maximum suppression algorithm is used to remove the bounding box to improve the detection effect. To reduce the manual labeling workload, an automatic labeling method is proposed. The improved Faster RCNN framework achieves the F1-score of more than 98% after identifying cells, and our method is more accurate than other advanced methods. In terms of three-dimensional counting algorithms, the cells are spatially tracked. Cell matching and correction are carried out by extracting multiple feature information of cells, and adhesion cells are extracted separately for priority processing. The algorithm carried out a tracking experiment on the image sequences of five groups of suspended cells, and the final tracking accuracy of the experiment reached more than 95%. The tracking accuracy is better than other advanced algorithms. Experimental results show that the algorithm achieves high-precision three-dimensional tracking and counting of suspended cells.