An Earth Mover's Distance-Based Self-Supervised Framework for Cellular Dynamic Grading in Live-Cell Imaging.

Cellular appearance and its dynamics frequently serve as a proxy measurement of live-cell physiological properties. The computational analysis of cell properties is considered to be a significant endeavor in biological and biomedical research. Deep learning has garnered considerable success across various fields.

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.

Cell dynamic morphology classification using deep convolutional neural networks.

Cell morphology is often used as a proxy measurement of cell status to understand cell physiology. Hence, interpretation of cell dynamic morphology is a meaningful task in biomedical research. Inspired by the recent success of deep learning, we here explore the application of convolutional neural networks (CNNs) to cell dynamic morphology classification.

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.

Cell dynamic morphology analysis by deep convolutional features.

Computational analysis of cell dynamic morphology in time-lapse image is a challenging task in biomedical research. Inspired by the recent success of deep learning, we investigate the possibility to apply a deep neural network to cell dynamic morphology analysis in this paper. Specifically, a contour spectrum is composed as the input of neural network to characterize cell spatiotemporal deformation, then a pre-trained convolutional neural network model is performed for automatic feature extraction.

Characterization of single cell dynamic morphology by local deformation pattern modeling.

Computational analysis of cell dynamic morphology in time-lapse images has become a new topic of biomedical research. For single cell, it is a challenging task to consider the spatial inconsistency and the temporal accumulation of cell deformation. This paper introduces an innovative automate analysis method, in which temporal features of contour point deformation are captured and then local deformation pattern is modeled to characterize cell dynamic morphology and predict cell activation statue.

Analyzing dynamic cellular morphology in time-lapsed images enabled by cellular deformation pattern recognition.

Computational analysis of cellular morphology aims to provide quantitative information of the global organizational and physiological state of cells, and has long been a major topic of biomedical research. Instead of analyzing morphology of static cells, we concentrate on live-cell deformation in a period of time. According to our observation of dynamic cell behavior, we have assumed that the pattern of cellular deformation is relevant to the cellular state.