A linear programming approach to reconstructing subcellular structures from confocal images for automated generation of representative 3D cellular models

Wood, Scott T.; Dean, Brian C.; Dean, Delphine

doi:10.1016/j.media.2012.12.002

Cited by 9 publications

(9 citation statements)

References 39 publications

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“…The proposed segmentation algorithm addresses the limitations of 3D objects based on confocal microscopic image stacks. A 3D reconstruction of subcellular object was performed by Wood ST et al [ 27 ], but they only reconstructed a single cell surrounded by background region. For a complex cell clump, however, automatic algorithms are only feasible under strict conditions.…”

Section: Discussionmentioning

confidence: 99%

3D Reconstruction of Coronary Artery Vascular Smooth Muscle Cells

2016

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AimsThe 3D geometry of individual vascular smooth muscle cells (VSMCs), which are essential for understanding the mechanical function of blood vessels, are currently not available. This paper introduces a new 3D segmentation algorithm to determine VSMC morphology and orientation.Methods and ResultsA total of 112 VSMCs from six porcine coronary arteries were used in the analysis. A 3D semi-automatic segmentation method was developed to reconstruct individual VSMCs from cell clumps as well as to extract the 3D geometry of VSMCs. A new edge blocking model was introduced to recognize cell boundary while an edge growing was developed for optimal interpolation and edge verification. The proposed methods were designed based on Region of Interest (ROI) selected by user and interactive responses of limited key edges. Enhanced cell boundary features were used to construct the cell’s initial boundary for further edge growing. A unified framework of morphological parameters (dimensions and orientations) was proposed for the 3D volume data. Virtual phantom was designed to validate the tilt angle measurements, while other parameters extracted from 3D segmentations were compared with manual measurements to assess the accuracy of the algorithm. The length, width and thickness of VSMCs were 62.9±14.9μm, 4.6±0.6μm and 6.2±1.8μm (mean±SD). In longitudinal-circumferential plane of blood vessel, VSMCs align off the circumferential direction with two mean angles of -19.4±9.3° and 10.9±4.7°, while an out-of-plane angle (i.e., radial tilt angle) was found to be 8±7.6° with median as 5.7°.ConclusionsA 3D segmentation algorithm was developed to reconstruct individual VSMCs of blood vessel walls based on optical image stacks. The results were validated by a virtual phantom and manual measurement. The obtained 3D geometries can be utilized in mathematical models and leads a better understanding of vascular mechanical properties and function.

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Section: Discussionmentioning

confidence: 99%

3D Reconstruction of Coronary Artery Vascular Smooth Muscle Cells

2016

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“…This constraint means that candidates {1, 4, 7} are not included in the optimal solution if A 2 is selected as a cell region. Linear programming (LP) is used in cell tracking [28][29] and segmentation methods [30] [31]. Al-Kofahi et.al.…”

Section: Select Optimal Set Of Cell Regionsmentioning

confidence: 99%

Cell Detection From Redundant Candidate Regions Under Nonoverlapping Constraints

Bise

Sato²

2015

IEEE Trans. Med. Imaging

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Cell detection in microscopy images is essential for automated cell behavior analysis including cell shape analysis and cell tracking. Robust cell detection in high-density and low-contrast images is still challenging since cells often touch and partially overlap, forming a cell cluster with blurry intercellular boundaries. In such cases, current methods tend to detect multiple cells as a cluster. If the control parameters are adjusted to separate the touching cells, other problems often occur: a single cell may be segmented into several regions, and cells in low-intensity regions may not be detected. To solve these problems, we first detect redundant candidate regions, which include many false positives but in turn very few false negatives, by allowing candidate regions to overlap with each other. Next, the score for how likely the candidate region contains the main part of a single cell is computed for each cell candidate using supervised learning. Then we select an optimal set of cell regions from the redundant regions under nonoverlapping constraints, where each selected region looks like a single cell and the selected regions do not overlap. We formulate this problem of optimal region selection as a binary linear programming problem under nonoverlapping constraints. We demonstrated the effectiveness of our method for several types of cells in microscopy images. Our method performed better than five representative methods, achieving an F-measure of over 0.9 for all data sets. Experimental application of the proposed method to 3-D images demonstrated that also works well for 3-D cell detection.

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“…For example, order parameters analysis has been used to describe the aggregate image texture and orientation, without explicit treatment of each discrete stress fibers [ 38 ], thus avoiding the challenging task of detecting and segmenting individual filaments. Alternatively, a simulation-based approach can be used to study the stress fiber networks based on Finite Element analysis of idealized cellular architectures [ 12 , 40 – 45 ]. Likewise, stress fiber networks can be treated as a micrograph-based linear superposition of filaments such that relevant coefficients can be solved by linear optimization [ 40 ].…”

Section: Introductionmentioning

confidence: 99%

An integrated enhancement and reconstruction strategy for the quantitative extraction of actin stress fibers from fluorescence micrographs

Zhang¹,

Xia²,

Kanchanawong

2017

BMC Bioinformatics

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BackgroundThe stress fibers are prominent organization of actin filaments that perform important functions in cellular processes such as migration, polarization, and traction force generation, and whose collective organization reflects the physiological and mechanical activities of the cells. Easily visualized by fluorescence microscopy, the stress fibers are widely used as qualitative descriptors of cell phenotypes. However, due to the complexity of the stress fibers and the presence of other actin-containing cellular features, images of stress fibers are relatively challenging to quantitatively analyze using previously developed approaches, requiring significant user intervention. This poses a challenge for the automation of their detection, segmentation, and quantitative analysis.ResultHere we describe an open-source software package, SFEX (Stress Fiber Extractor), which is geared for efficient enhancement, segmentation, and analysis of actin stress fibers in adherent tissue culture cells. Our method made use of a carefully chosen image filtering technique to enhance filamentous structures, effectively facilitating the detection and segmentation of stress fibers by binary thresholding. We subdivided the skeletons of stress fiber traces into piecewise-linear fragments, and used a set of geometric criteria to reconstruct the stress fiber networks by pairing appropriate fiber fragments. Our strategy enables the trajectory of a majority of stress fibers within the cells to be comprehensively extracted. We also present a method for quantifying the dimensions of the stress fibers using an image gradient-based approach. We determine the optimal parameter space using sensitivity analysis, and demonstrate the utility of our approach by analyzing actin stress fibers in cells cultured on various micropattern substrates.ConclusionWe present an open-source graphically-interfaced computational tool for the extraction and quantification of stress fibers in adherent cells with minimal user input. This facilitates the automated extraction of actin stress fibers from fluorescence images. We highlight their potential uses by analyzing images of cells with shapes constrained by fibronectin micropatterns. The method we reported here could serve as the first step in the detection and characterization of the spatial properties of actin stress fibers to enable further detailed morphological analysis.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-017-1684-y) contains supplementary material, which is available to authorized users.

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A linear programming approach to reconstructing subcellular structures from confocal images for automated generation of representative 3D cellular models

Cited by 9 publications

References 39 publications

3D Reconstruction of Coronary Artery Vascular Smooth Muscle Cells

3D Reconstruction of Coronary Artery Vascular Smooth Muscle Cells

Cell Detection From Redundant Candidate Regions Under Nonoverlapping Constraints

An integrated enhancement and reconstruction strategy for the quantitative extraction of actin stress fibers from fluorescence micrographs

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