10,179 research outputs found
An optimal control approach to cell tracking
Cell tracking is of vital importance in many biological studies, hence robust cell tracking algorithms are needed for inference of dynamic features from (static) in vivo and in vitro experimental imaging data of cells migrating.
In recent years much attention has been focused on the modelling of cell motility from physical principles and the development of state-of-the art numerical methods for the simulation of the model equations. Despite this, the vast majority of cell tracking algorithms proposed to date focus solely on the imaging data itself and do not attempt to incorporate any physical knowledge on cell migration into the tracking procedure.
In this study, we present a mathematical approach for cell tracking, in which we formulate the cell tracking problem as an inverse problem for fitting a mathematical model for cell motility to experimental imaging data. The novelty of this approach is that the physics underlying the model for cell migration is encoded in the tracking algorithm. To illustrate this we focus on an example of Zebrafish (Danio rerio's larvae) Neutrophil migration and contrast an ad-hoc approach to cell tracking based on interpolation with the model fitting approach we propose in this study
A pattern-based approach to a cell tracking ontology
Time-lapse microscopy has thoroughly transformed our understanding of biological motion and developmental dynamics from single cells to entire organisms. The increasing amount of cell tracking data demands the creation of tools to make extracted data searchable and interoperable between experiment and data types. In order to address that problem, the current paper reports on the progress in building the Cell Tracking Ontology (CTO): An ontology framework for describing, querying and integrating data from complementary experimental techniques in the domain of cell tracking experiments. CTO is based on a basic knowledge structure: the cellular genealogy serving as a backbone model to integrate specific biological ontologies into tracking data. As a first step we integrate the Phenotype and Trait Ontology (PATO) as one of the most relevant ontologies to annotate cell tracking experiments. The CTO requires both the integration of data on various levels of generality as well as the proper structuring of collected information. Therefore, in order to provide a sound foundation of the ontology, we have built on the rich body of work on top-level ontologies and established three generic ontology design patterns addressing three modeling challenges for properly representing cellular genealogies, i.e. representing entities existing in time, undergoing changes over time and their organization into more complex structures such as situations
Magnetic Particle Imaging tracks the long-term fate of in vivo neural cell implants with high image contrast.
We demonstrate that Magnetic Particle Imaging (MPI) enables monitoring of cellular grafts with high contrast, sensitivity, and quantitativeness. MPI directly detects the intense magnetization of iron-oxide tracers using low-frequency magnetic fields. MPI is safe, noninvasive and offers superb sensitivity, with great promise for clinical translation and quantitative single-cell tracking. Here we report the first MPI cell tracking study, showing 200-cell detection in vitro and in vivo monitoring of human neural graft clearance over 87 days in rat brain
Cell Segmentation and Tracking using CNN-Based Distance Predictions and a Graph-Based Matching Strategy
The accurate segmentation and tracking of cells in microscopy image sequences
is an important task in biomedical research, e.g., for studying the development
of tissues, organs or entire organisms. However, the segmentation of touching
cells in images with a low signal-to-noise-ratio is still a challenging
problem. In this paper, we present a method for the segmentation of touching
cells in microscopy images. By using a novel representation of cell borders,
inspired by distance maps, our method is capable to utilize not only touching
cells but also close cells in the training process. Furthermore, this
representation is notably robust to annotation errors and shows promising
results for the segmentation of microscopy images containing in the training
data underrepresented or not included cell types. For the prediction of the
proposed neighbor distances, an adapted U-Net convolutional neural network
(CNN) with two decoder paths is used. In addition, we adapt a graph-based cell
tracking algorithm to evaluate our proposed method on the task of cell
tracking. The adapted tracking algorithm includes a movement estimation in the
cost function to re-link tracks with missing segmentation masks over a short
sequence of frames. Our combined tracking by detection method has proven its
potential in the IEEE ISBI 2020 Cell Tracking Challenge
(http://celltrackingchallenge.net/) where we achieved as team KIT-Sch-GE
multiple top three rankings including two top performances using a single
segmentation model for the diverse data sets.Comment: 25 pages, 14 figures, methods of the team KIT-Sch-GE for the IEEE
ISBI 2020 Cell Tracking Challeng
Globally Optimal Cell Tracking using Integer Programming
We propose a novel approach to automatically tracking cell populations in
time-lapse images. To account for cell occlusions and overlaps, we introduce a
robust method that generates an over-complete set of competing detection
hypotheses. We then perform detection and tracking simultaneously on these
hypotheses by solving to optimality an integer program with only one type of
flow variables. This eliminates the need for heuristics to handle missed
detections due to occlusions and complex morphology. We demonstrate the
effectiveness of our approach on a range of challenging sequences consisting of
clumped cells and show that it outperforms state-of-the-art techniques.Comment: Engin T\"uretken and Xinchao Wang contributed equally to this wor
Propagation of numerical noise in particle-in-cell tracking
Particle-in-cell (PIC) is the most used algorithm to perform self-consistent
tracking of intense charged particle beams. It is based on depositing
macro-particles on a grid, and subsequently solving on it the Poisson equation.
It is well known that PIC algorithms occupy intrinsic limitations as they
introduce numerical noise. Although not significant for short-term tracking,
this becomes important in simulations for circular machines over millions of
turns as it may induce artificial diffusion of the beam.
In this work, we present a modeling of numerical noise induced by PIC
algorithms, and discuss its influence on particle dynamics. The combined effect
of particle tracking and noise created by PIC algorithms leads to correlated or
decorrelated numerical noise. For decorrelated numerical noise we derive a
scaling law for the simulation parameters, allowing an estimate of artificial
emittance growth. Lastly, the effect of correlated numerical noise is
discussed, and a mitigation strategy is proposed.Comment: 14 pages, 12 figure
An eco-solution for track & trace of goods and third party logistics
This paper presents a new economic cost-effective solution known as the Web and telephony based method for tracking and tracing of goods and small and medium sized third party logistic providers. Considering that these companies usually operate on very flat margins, a comparison is made of the available track and trace technologies like GPS, mobile phone approximated GPS and Java based cell tracking in terms of costs, operating risks, and other evaluation criteria
Nanotechnology-Assisted Cell Tracking
The usefulness of nanoparticles (NPs) in the diagnostic and/or therapeutic sector is derived from their aptitude for navigating intra-and extracellular barriers successfully and to be spatiotemporally targeted. In this context, the optimization of NP delivery platforms is technologically related to the exploitation of the mechanisms involved in the NP–cell interaction. This review provides a detailed overview of the available technologies focusing on cell–NP interaction/detection by describing their applications in the fields of cancer and regenerative medicine. Specifically, a literature survey has been performed to analyze the key nanocarrier-impacting elements, such as NP typology and functionalization, the ability to tune cell interaction mechanisms under in vitro and in vivo conditions by framing, and at the same time, the imaging devices supporting NP delivery assessment, and consideration of their specificity and sensitivity. Although the large amount of literature information on the designs and applications of cell membrane-coated NPs has reached the extent at which it could be considered a mature branch of nanomedicine ready to be translated to the clinic, the technology applied to the biomimetic functionalization strategy of the design of NPs for directing cell labelling and intracellular retention appears less advanced. These approaches, if properly scaled up, will present diverse biomedical applications and make a positive impact on human health
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