Bioimage Analysis and Cell Motility

Boquet-Pujadas, Aleix; Olivo‐Marin, Jean‐Christophe; Guillén, Nancy

doi:10.1016/j.patter.2020.100170

Cited by 17 publications

(13 citation statements)

References 177 publications

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“…The top four rows show examples of predicted shapes overlaid on the true shape for different values of p. For each sequence, the red curves denote the predicted shapes overlaid on the true shapes. On close inspection, one can see that the prediction error for VAR (1) is smaller than that for other lag values. The bottom plot quantifies this and shows prediction errors for different values of p on the test part of this sequence.…”

Section: B Predicting Future Shapesmentioning

confidence: 95%

“…U NDERSTANDING cell motility is crucial to understanding such critical biological phenomena as development, reproduction, healing, and cancer metastasis, among others. Cell motility is a complex process with many simultaneous events, including certain intra-and extra-cellular physiochemical events, that lead to a restructuring of the cytoskeleton [1]. This restructuring, in turn, induces morphological and positional changes in cells causing motility and complex migration patterns.…”

Section: Introductionmentioning

confidence: 99%

“…However, scientists hypothesize that biological influences force certain characteristic structures on motility patterns. The cell motility has two components: (1) kinematics, relating to the gross movement (position, speed, rotations, etc.) of the cell, and (2) morphology, relating to changes in its shape.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Dynamic Shape Modeling to Analyze Modes ofMigration During Cell Motility

Deng,

Sarkar,

Labruyere

et al. 2021

Preprint

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This paper develops a generative statistical model for representing, modeling, and comparing the morphological evolution of biological cells undergoing motility. It uses the elastic shape analysis to separate cell kinematics (overall location, rotation, speed, etc.) from its morphology and represents morphological changes using transported square-root vector fields (TSRVFs). This TSRVF representation, followed by a PCAbased dimension reduction, provides a convenient mathematical representation of a shape sequence in the form of a Euclidean time series. Fitting a vector auto-regressive (VAR) model to this TSRVF-PCA time series leads to statistical modeling of the overall shape dynamics. We use the parameters of the fitted VAR model to characterize morphological evolution. We validate VAR models through model comparisons, synthesis, and sequence classifications. For classification, we use the VAR parameters in conjunction with different classifiers: SVM, Random Forest, and CNN, and obtain high classification rates. Extensive experiments presented here demonstrate the success of the proposed pipeline. These results are the first of the kind in classifying cell migration videos using shape dynamics.

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Section: B Predicting Future Shapesmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

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Dynamic Shape Modeling to Analyze Modes ofMigration During Cell Motility

Deng,

Sarkar,

Labruyere

et al. 2021

Preprint

Self Cite

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“…On the other hand, bioimage analysis includes a myriad of methods and techniques for different purposes, such as object detection, localization, and segmentation [ 388–391 ]. Some of these procedures are based on artificial intelligence.…”

Section: Conclusion and Future Perspectivesmentioning

confidence: 99%

Unravelling cell migration: defining movement from the cell surface

Merino-Casallo

Gómez‐Benito

Hervas-Raluy

et al. 2022

Cell Adhesion & Migration

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Cell motility is essential for life and development. Unfortunately, cell migration is also linked to several pathological processes, such as cancer metastasis. Cells’ ability to migrate relies on many actors. Cells change their migratory strategy based on their phenotype and the properties of the surrounding microenvironment. Cell migration is, therefore, an extremely complex phenomenon. Researchers have investigated cell motility for more than a century. Recent discoveries have uncovered some of the mysteries associated with the mechanisms involved in cell migration, such as intracellular signaling and cell mechanics. These findings involve different players, including transmembrane receptors, adhesive complexes, cytoskeletal components , the nucleus, and the extracellular matrix. This review aims to give a global overview of our current understanding of cell migration.

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“…In medical imaging for instance, this two-step strategy allows measuring the stiffness of tissue to detect tumours in a process known as elastography [4], [8], [9] or studying blood flow [10], [11]; whereas in meteorology it allows assimilating atmospheric pressures and wind speed [12], [13]. A similar approach is used in biology to study cytoplasmic pressures during migration and development by first extracting the corresponding motion field using particle image velocimetry (PIV) [14], [15]. However, the most prevalent example in cell science is perhaps Traction Force Microscopy (TFM, Figure 1), a technique whereby the deformation of a soft substrate seeded with fluorescent beads is used to estimate the traction forces exerted thereon [16].…”

Section: Introductionmentioning

confidence: 99%

Reformulating Optical Flow to Solve Image-Based Inverse Problems and Quantify Uncertainty

Boquet-Pujadas

Olivo‐Marin

2023

IEEE Trans. Pattern Anal. Mach. Intell.

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From meteorology to medical imaging and cell mechanics, many scientific domains use inverse problems (IPs) to extract physical measurements from image movement. To this end, motion estimation methods such as optical flow (OF) pre-process images into motion data to feed the IP, which then inverts for the measurements through a physical model. However, this combined OFIP pipeline exacerbates the ill-posedness inherent to each technique, propagating errors and preventing uncertainty quantification. We introduce a Bayesian PDE-constrained framework that transforms visual information directly into physical measurements in the context of probability distributions. The posterior mean is a constrained IP that tracks brightness while satisfying the physical model, thereby translating the aperture problem from the motion to the underlying physics; whereas the posterior covariance derives measurement error out of image noise. As we illustrate with traction force microscopy, our approach offers several advantages: more accurate reconstructions; unprecedented flexibility in experiment design (e.g. arbitrary boundary conditions); and the exclusivity of measurement error, central to empirical science, yet still unavailable under the OFIP strategy.

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Bioimage Analysis and Cell Motility

Cited by 17 publications

References 177 publications

Dynamic Shape Modeling to Analyze Modes ofMigration During Cell Motility

Dynamic Shape Modeling to Analyze Modes ofMigration During Cell Motility

Unravelling cell migration: defining movement from the cell surface

Reformulating Optical Flow to Solve Image-Based Inverse Problems and Quantify Uncertainty

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