Quantitative analysis of lymphocytes morphology and motion in intravital microscopic images

Analyzing temporal dynamics of cell deformation and intracellular movement with video feature aggregation

BACKGROUND

The 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.

METHODS

This 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.

RESULTS

Our 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.

CONCLUSION

Experimental 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.

Computational Analysis of Cell Dynamics in Videos with Hierarchical-Pooled Deep-Convolutional Features

Computational analysis of cellular appearance and its dynamics is used to investigate physiological properties of cells in biomedical research. In consideration of the great success of deep learning in video analysis, we first introduce two-stream convolutional networks (ConvNets) to automatically learn the biologically meaningful dynamics from raw live-cell videos. However, the two-stream ConvNets lack the ability to capture long-range video evolution. Therefore, a novel hierarchical pooling strategy is proposed to model the cell dynamics in a whole video, which is composed of trajectory pooling for short-term dynamics and rank pooling for long-range ones. Experimental results demonstrate that the proposed pipeline effectively captures the spatiotemporal dynamics from the raw live-cell videos and outperforms existing methods on our cell video database.

Multi-classification of cell deformation based on object alignment and run length statistic

Cellular morphology is widely applied in digital pathology and is essential for improving our understanding of the basic physiological processes of organisms. One of the main issues of application is to develop efficient methods for cell deformation measurement. We propose an innovative indirect approach to analyze dynamic cell morphology in image sequences. The proposed approach considers both the cellular shape change and cytoplasm variation, and takes each frame in the image sequence into account. The cell deformation is measured by the minimum energy function of object alignment, which is invariant to object pose. Then an indirect analysis strategy is employed to overcome the limitation of gradual deformation by run length statistic. We demonstrate the power of the proposed approach with one application: multi-classification of cell deformation. Experimental results show that the proposed method is sensitive to the morphology variation and performs better than standard shape representation methods.