Online 3-D tracking of suspension living cells imaged with phase-contrast microscopy

Cell Population Tracking in a Honeycomb Structure Using an IMM Filter Based 3D Local Graph Matching Model

Developing algorithms for plant cell population tracking is very critical for the modeling of plant cell growth pattern and gene expression dynamics. The tracking of plant cells in microscopic image stacks is very challenging for several reasons: (1) plant cells are densely packed in a specific honeycomb structure; (2) they are frequently dividing; and (3) they are imaged in different layers within 3D image stacks. Based on an existing 2D local graph matching algorithm, this paper focuses on building a 3D plant cell matching model, by exploiting the cells' 3D spatiotemporal context. Furthermore, the Interacting Multi-Model filter (IMM) is combined with the 3D local graph matching model to track the plant cell population simultaneously. Because our tracking algorithm does not require the identification of "tracking seeds", the tracking stability and efficiency are greatly enhanced. Last, the plant cell lineages are achieved by associating the cell tracklets, using a maximum-a-posteriori (MAP) method. Compared with the 2D matching method, the experimental results on multiple datasets show that our proposed approach does not only greatly improve the tracking accuracy by 18 percent, but also successfully tracks the plant cells located at the high curvature primordial region, which is not addressed in previous work.

Multicell migration tracking within angiogenic networks by deep learning-based segmentation and augmented Bayesian filtering

Cell migration is a key feature for living organisms. Image analysis tools are useful in studying cell migration in three-dimensional (3-D) in vitro environments. We consider angiogenic vessels formed in 3-D microfluidic devices (MFDs) and develop an image analysis system to extract cell behaviors from experimental phase-contrast microscopy image sequences. The proposed system initializes tracks with the end-point confocal nuclei coordinates. We apply convolutional neural networks to detect cell candidates and combine backward Kalman filtering with multiple hypothesis tracking to link the cell candidates at each time step. These hypotheses incorporate prior knowledge on vessel formation and cell proliferation rates. The association accuracy reaches 86.4% for the proposed algorithm, indicating that the proposed system is able to associate cells more accurately than existing approaches. Cell culture experiments in 3-D MFDs have shown considerable promise for improving biology research. The proposed system is expected to be a useful quantitative tool for potential microscopy problems of MFDs.

Purification, Visualization, and Molecular Signature of Neural Stem Cells

Neural stem cells (NSCs) are isolated from primary brain tissue and propagated as a heterogeneous mix of cells, including neural progenitors. To date, NSCs have not been purified in vitro to allow study of their biology and utility in regenerative medicine. In this study, we identify C1qR1 as a novel marker for NSCs and show that it can be used along with Lewis-X (LeX) to yield a highly purified population of NSCs. Using time-lapse microscopy, we are able to follow NSCs forming neurospheres, allowing their visualization. Finally, using single-cell polymerase chain reaction (PCR), we determine the molecular signature of NSCs. The single-cell PCR data suggest that along with the Notch and Shh pathways, the Hippo pathway plays an important role in NSC activity.

An analysis-synthesis approach for neurosphere modelisation under phase-contrast microscopy

The study of stem cells is one of the most important biomedical research. Understanding their development could allow multiple applications in regenerative medicine. For this purpose, automated solutions for the observation of stem cell development process are needed. This study introduces an on-line analysis method for the modelling of neurosphere evolution during the early time of their development under phase contrast microscopy. From the corresponding phase contrast time-lapse sequences, we extract information from the neurosphere using a combination of phase contrast physics deconvolution and curve detection for locate the cells inside the neurosphere. Then, based on prior biological knowledge, we generate possible and optimal 3-dimensional configuration using 2D to 3D registration methods and evolutionary optimisation algorithm.