RT Journal Article
SR Electronic
T1 Label-Free Mammalian Cell Tracking Enhanced by Precomputed Velocity Fields
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2023.01.25.525598
DO 10.1101/2023.01.25.525598
A1 Yue Han
A1 Yang Lei
A1 Viktor Shkolnikov
A1 Daisy Xin
A1 Steven Barcelo
A1 Jan Allebach
A1 Edward J. Delp
YR 2023
UL http://biorxiv.org/content/early/2023/01/26/2023.01.25.525598.abstract
AB Label-free cell imaging, where the cell is not “labeled” or modified by fluorescent chemicals, is an important research area in the field of biology. It avoids altering the cell’s properties which typically happens in the process of chemical labeling. However, without the contrast enhancement from the label, the analysis of label-free imaging is more challenging than label-based imaging. In addition, it provides few human interpretable features, and thus needs machine learning approaches to help with the identification and tracking of specific cells. We are interested in label-free phase contrast imaging to track cells flowing in a cell sorting device where images are acquired at 500 frames/s. Existing Multiple Object Tracking (MOT) methods face four major challenges when used for tracking cells in a microfluidic sorting device: (i) most of the cells have large displacements between frames without any overlap; (ii) it is difficult to distinguish between cells as they are visually similar to each other; (iii) the velocities of cells vary with the location in the device; (iv) the appearance of cells may change as they move in and out of the focal plane of the imaging sensor that observes the isolation process. In this paper, we introduce a method for tracking cells in a predefined flow in the sorting device via phase contrast microscopy. Our proposed method is based on DeepSORT and YOLOv4 and exploits prior knowledge of a cell’s velocity to assist tracking. We modify the Kalman filter in DeepSORT to accommodate a non-constant velocity motion model and integrate a representative velocity field obtained from fluid dynamics into the Kalman filter. The experimental results show that our proposed method outperforms several MOT methods for tracking cells in the sorting device.Competing Interest StatementThe authors have declared no competing interest.