Studying the numeration methods of signals with unstable background for in vivo flow cytometry

In recent years, the in vivo flow cytometry (IVFC) has been a useful technology in detecting and quantifying the circulating cells dynamically in living animals, especially in the research related to the cell tracking and the cancer metastasis. In practice, however, the unstable background signals caused by the experiment animals’ respiratory movement, limb movement and photo-bleaching of tissues’ auto-fluorescence exist in many IVFC data, which could affect the accuracy of cell counting results in the following post-processing procedure, making the IVFC signals less available. Here we developed a signal processing method that could effectively correct the unstable background signals by using methods combining interpolating, fitting, automatic segmenting and wavelet-based denoising. Compared with the previously used non-correction methods, i.e., the “line-gating” method or the automatic threshold method, this method showed a higher accuracy and efficiency in counting cell numbers of IVFC signals, as well as demonstrating a better statistic results in the Pearson’s correlation coefficient R2 and the mean-squared error (MSE).