Segmentation and tracking of Pseudomonas aeruginosa for cell dynamics analysis in time-lapse images

Chen, Jianxu; Cai, Yiqing; Chen, Wei; Yang, Lin; Alber, Mark; Chen, Danny Z.

doi:10.1109/isbi.2016.7493426

Cited by 3 publications

(5 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A challenge with tracking single-cell behavior for P. aeruginosa and most swarming species is the difficulty in consistently tracking the same cell over time within these high-cell-density swarm communities at the single-cell level (Movie S2). For example, we employed our existing cell behavior discernment algorithm (37) to track a subset of cells from Movie S2 and found that only 20 of 70 (29%) cell trajectories were correctly tracked with no manual correction.…”

Section: Resultsmentioning

confidence: 99%

“…Image processing, analysis, and bacterium tracking. We utilized an approach to discern and track cells from time-lapse images developed previously in our research group (37). The movement characteristics of individual cells were discerned from fluorescence microscopy time-lapse images by adopting a two-stage approach enabling automatic distinction and tracking of single cells (Fig.…”

Section: Methodsmentioning

confidence: 99%

“…Equations 2a and 2b required that all the points in each cell p i in frame T should find a position to move to in the next frame, T ϩ 1, and that all the points in each cell q j of frame T ϩ 1 should find where they were moved from in the previous frame T. Parameters w p ij and w q ij were scaling factors computed based on the sizes of p i and q j . These scaling factors were designed primarily to accommodate cell division and the potential size difference of the same physical cell segmented in two different frames due to discretization and imaging artifacts while imposing a size change limit from one frame to another for the purpose of consistently tracking the same cells (37). We ultimately determined the position history for each identified cell in polynomial time by linear programming (53) for every two consecutive image frames T and T ϩ 1 in each time-lapse image movie.…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Single Cells Exhibit Differing Behavioral Phases during Early Stages of Pseudomonas aeruginosa Swarming

et al. 2019

Self Cite

View full text Add to dashboard Cite

Pseudomonas aeruginosa is among the many bacteria that swarm, where groups of cells coordinate to move over surfaces. It has been challenging to determine the behavior of single cells within these high-cell-density swarms. To track individual cells within P. aeruginosa swarms, we imaged a fluorescently labeled subset of the larger population. Single cells at the advancing swarm edge varied in their motility dynamics as a function of time. From these data, we delineated four phases of early swarming prior to the formation of the tendril fractals characteristic of P. aeruginosa swarming by collectively considering both micro- and macroscale data. We determined that the period of greatest single-cell motility does not coincide with the period of greatest collective swarm expansion. We also noted that flagellar, rhamnolipid, and type IV pilus motility mutants exhibit substantially less single-cell motility than the wild type. IMPORTANCE Numerous bacteria exhibit coordinated swarming motion over surfaces. It is often challenging to assess the behavior of single cells within swarming communities due to the limitations of identifying, tracking, and analyzing the traits of swarming cells over time. Here, we show that the behavior of Pseudomonas aeruginosa swarming cells can vary substantially in the earliest phases of swarming. This is important to establish that dynamic behaviors should not be assumed to be constant over long periods when predicting and simulating the actions of swarming bacteria.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Single Cells Exhibit Differing Behavioral Phases during Early Stages of Pseudomonas aeruginosa Swarming

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…1) Datasets: We evaluate our H-EMD on six 2D+time cell video datasets, including two in-house datasets (P. aeruginosa [54] and M. xanthus [44]) and four public datasets from the Cell Tracking Challenge [53] (Fluo-N2DL-HeLa, PhC-C2DL-PSC, PhC-C2DH-U373, and Fluo-N2DH-SIM+), and two 3D datasets, including one in-house Fungus [52], [55], [56] For the in-house datasets, instance segmentation annotations were manually labeled by experts. For the public datasets from the Cell Tracking Challenge, three types of instance segmentation annotations are provided for the training sequences: ground truth, gold truth, and silver truth.…”

Section: Methodsmentioning

confidence: 99%

“…aeruginosa. The in-house P. aeruginosa dataset [54] contains two videos of 2D fluorescence microscopy images for segmenting dynamic P. aeruginosa cells. One video of 100 frames is used for training (400 × 400 pixels per frame), and the other video of 40 frames is for testing (513 × 513 pixels per frame).…”

Section: Methodsmentioning

confidence: 99%

H-EMD: A Hierarchical Earth Mover’s Distance Method for Instance Segmentation

Liang¹,

Zhang²,

Ding³

et al. 2022

IEEE Trans. Med. Imaging

Self Cite

View full text Add to dashboard Cite

Deep learning (DL) based semantic segmentation methods have achieved excellent performance in biomedical image segmentation, producing high quality probability maps to allow extraction of rich instance information to facilitate good instance segmentation. While numerous efforts were put into developing new DL semantic segmentation models, less attention was paid to a key issue of how to effectively explore their probability maps to attain the best possible instance segmentation. We observe that probability maps by DL semantic segmentation models can be used to generate many possible instance candidates, and accurate instance segmentation can be achieved by selecting from them a set of "optimized" candidates as output instances. Further, the generated instance candidates form a well-behaved hierarchical structure (a forest), which allows selecting instances in an optimized manner. Hence, we propose a novel framework, called hierarchical earth mover's distance (H-EMD), for instance segmentation in biomedical 2D+time videos and 3D images, which judiciously incorporates consistent instance selection with semantic-segmentation-generated probability maps. H-EMD contains two main stages. (1) Instance candidate generation: capturing instance-structured information in probability maps by generating many instance candidates in a forest structure. (2) Instance candidate selection: selecting instances from the candidate set for final instance segmentation. We formulate a key instance selection problem on the instance candidate forest as an optimization problem based on the earth mover's distance (EMD), and solve it by integer linear programming. Extensive experiments on eight biomedical video or 3D datasets demonstrate that H-EMD consistently boosts DL semantic segmentation models and is highly competitive with stateof-the-art methods.

show abstract

AntVis: A web-based visual analytics tool for exploring ant movement data

Zheng

Chen

et al. 2020

Visual Informatics

View full text Add to dashboard Cite

Segmentation and tracking of Pseudomonas aeruginosa for cell dynamics analysis in time-lapse images

Cited by 3 publications

References 5 publications

Single Cells Exhibit Differing Behavioral Phases during Early Stages of Pseudomonas aeruginosa Swarming

Single Cells Exhibit Differing Behavioral Phases during Early Stages of Pseudomonas aeruginosa Swarming

H-EMD: A Hierarchical Earth Mover’s Distance Method for Instance Segmentation

AntVis: A web-based visual analytics tool for exploring ant movement data

Contact Info

Product

Resources

About