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Abstract
BackgroundThe research and analysis of cellular physiological properties has been an essential approach to studying some biological and biomedical problems. Temporal dynamics of cells therein are used as a quantifiable indicator of cellular response to extracellular cues and physiological stimuli.MethodsThis work presents a novel image-based framework to profile and model the cell dynamics in live-cell videos. In the framework, the cell dynamics between frames are represented as frame-level features from cell deformation and intracellular movement. On the one hand, shape context is introduced to enhance the robustness of measuring the deformation of cellular contours. On the other hand, we employ Scale-Invariant Feature Transform (SIFT) flow to simultaneously construct the complementary movement field and appearance change field for the cytoplasmic streaming. Then, time series modeling is performed on these frame-level features. Specifically, temporal feature aggregation is applied to capture the video-wide temporal evolution of cell dynamics.ResultsOur results demonstrate that the proposed cell dynamic features can effectively capture the cell dynamics in videos. They also prove that the Movement Field and Appearance Change Field Feature (MFAFF) can more precisely model the cytoplasmic streaming. Besides, temporal aggregation of cell dynamic features brings a substantial absolute increase of classification performance.ConclusionExperimental results demonstrate that the proposed framework outperforms competing mainstreaming approaches on the aforementioned datasets. Thus, our method has potential for cell dynamics analysis in videos.
Highlights
The research and analysis of cellular physiological properties has been an essential approach to studying some biological and biomedical problems
We introduce the compact encoding for the sake of modeling the temporal dynamics in live-cell videos
Experimental results we present a detailed experimental evaluation of our proposed framework based on the cell-video datasets in “Data” section
Summary
The research and analysis of cellular physiological properties has been an essential approach to studying some biological and biomedical problems. Image-based cell profiling provides quantitative information about cell state and paves the way to studying biological and biomedical problems [1,2,3,4]. As one of most significant aspects therein, characterizing temporal dynamics of cells is used to model cell cycle, analyze migratory phenotypes, and unravel cellular response to physiological stimuli [5–. To obtain the features for temporal dynamics of cells, cell profiling methods need to precisely characterize the visual appearance of cells and its change on consecutive frames. These methods are divided into two categories according to the cell dynamics they adopted (the deformation of cell contour and the active or directed intracellular movement). The length variation of protrusions (or the number variation of protrusions1) between frames is calculated as the feature of cell contour dynamics
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