Actin Filament Segmentation Using Dynamic Programming

Li, Hongsheng; Shen, Tian; Huang, Xiaolei

doi:10.1007/978-3-642-22092-0_34

Cited by 6 publications

(6 citation statements)

References 14 publications

(22 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The majority of studies on imaging F-actin have focused so far on the detection, extraction and rendering of actin micro-filament organization within cells. For example, Hongsheng et al have performed several studies on detecting actin filaments [3,4,5,6]. They used dynamic programming to estimate actin filament body points from an image sequence where initial locations are known [3].…”

Section: Previous/related Workmentioning

confidence: 99%

Robust detection and visualization of cytoskeletal structures in fibrillar scaffolds from 3-dimensional confocal image

Park

Jones

Moldovan

et al. 2013

2013 IEEE Symposium on Biological Data Visualization (BioVis)

View full text Add to dashboard Cite

Figure 1: Modes of F-actin organization in fiber-attaching cells. (a) Longitudinally oriented stress fibers (arrows) in a loosely attached cell, at 4h after scaffold seeding. In this cell, F-actin containing ruffles are also visible (arrowheads). (b) Concave actin bundles (CAB) in a cell cultivated in the scaffold for 10 days (arrowheads). Circular distribution of F-actin in CAB is shown in optical cross-sections across the supporting fiber (arrows). F-actin was stained by FITC-phalloidin (green), and the nuclei with DAPI (blue).

show abstract

Section: Previous/related Workmentioning

confidence: 99%

Robust detection and visualization of cytoskeletal structures in fibrillar scaffolds from 3-dimensional confocal image

Park

Jones

Moldovan

et al. 2013

2013 IEEE Symposium on Biological Data Visualization (BioVis)

View full text Add to dashboard Cite

show abstract

“…Li et al . [13–16] have conducted several studies on detecting actin filaments. In [13], the authors used a dynamic programming to estimate actin filament body points from an image sequence where initial locations are known.…”

Section: Discussionmentioning

confidence: 99%

“…[13–16] have conducted several studies on detecting actin filaments. In [13], the authors used a dynamic programming to estimate actin filament body points from an image sequence where initial locations are known. In [14], they used spatiotemporal active‐surface and active‐contour models to find the extreme locations of the filaments in 2D time‐lapse image sequences.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Use of artificial neural networks to identify and analyze polymerized actin‐based cytoskeletal structures in 3D confocal images

Park

2023

Quant. Biol.

View full text Add to dashboard Cite

We propose an artificial neural network (ANN) as a kernel function of the recognizer of legitimate CABs from candidate CABs, that does not need human interventions. The performance of the recognizer shows noticeable recognition accuracy and addresses shortcomings of previous methods, including the need for human visual validation to recognize CABs from candidate CABs. Further, it helps to find and reduce errors resulting from human visual validation, which in turn would provide biologists/biophysicists a more comprehensive. understanding of a CAB.BackgroundLiving cells need to undergo subtle shape adaptations in response to the topography of their substrates. These shape changes are mainly determined by reorganization of their internal cytoskeleton, with a major contribution from filamentous (F) actin. Bundles of F‐actin play a major role in determining cell shape and their interaction with substrates, either as “stress fibers,” or as our newly discovered “Concave Actin Bundles” (CABs), which mainly occur while endothelial cells wrap micro‐fibers in culture.MethodsTo better understand the morphology and functions of these CABs, it is necessary to recognize and analyze as many of them as possible in complex cellular ensembles, which is a demanding and time‐consuming task. In this study, we present a novel algorithm to automatically recognize CABs without further human intervention. We developed and employed a multilayer perceptron artificial neural network (“the recognizer”), which was trained to identify CABs.ResultsThe recognizer demonstrated high overall recognition rate and reliability in both randomized training, and in subsequent testing experiments.ConclusionIt would be an effective replacement for validation by visual detection which is both tedious and inherently prone to errors.

show abstract

“…3). Similar to the path tracing for actin filament segmentation [11], we close the boundary by dynamic programming. For each gap, we create a directed graph that connects two points, the sink and source nodes, that delineate the gap.…”

Section: Gap Completion With Shortest Pathmentioning

confidence: 99%

Hierarchical Partial Matching and Segmentation of Interacting Cells

Gurari

Wong

et al. 2012

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012

View full text Add to dashboard Cite

Abstract. We propose a method that automatically tracks and segments living cells in phase-contrast image sequences, especially for cells that deform and interact with each other or clutter. We formulate the problem as a many-to-one elastic partial matching problem between closed curves. We introduce Double Cyclic Dynamic Time Warping for the scenario where a collision event yields a single boundary that encloses multiple touching cells and that needs to be cut into separate cell boundaries. The resulting individual boundaries may consist of segments to be connected to produce closed curves that match well with the individual cell boundaries before the collision event. We show how to convert this partial-curve matching problem into a shortest path problem that we then solve efficiently by reusing the computed shortest path tree. We also use our shortest path algorithm to fill the gaps between the segments of the target curves. Quantitative results demonstrate the benefit of our method by showing maintained accurate recognition of individual cell boundaries across 8068 images containing multiple cell interactions.

show abstract

Actin Filament Segmentation Using Dynamic Programming

Cited by 6 publications

References 14 publications

Robust detection and visualization of cytoskeletal structures in fibrillar scaffolds from 3-dimensional confocal image

Robust detection and visualization of cytoskeletal structures in fibrillar scaffolds from 3-dimensional confocal image

Use of artificial neural networks to identify and analyze polymerized actin‐based cytoskeletal structures in 3D confocal images

Hierarchical Partial Matching and Segmentation of Interacting Cells

Contact Info

Product

Resources

About