Automatic wavelet-based 3D nuclei segmentation and analysis for multicellular embryo quantification

Wu, Tzu‐Ching; Wang, Xu; Li, Linlin; Bu, Ye; Umulis, David M.

doi:10.1038/s41598-021-88966-2

Cited by 8 publications

(4 citation statements)

References 52 publications

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“…P-Smad image data that contains specific information on BMP signaling in space and time were quantitatively analyzed with our nuclei segmentation method (Wu et al, 2021;Zinski et al, 2017). The P-Smad data was applied as a scalar for the datamodel comparisons against the wild-type signaling profiles.…”

Section: Wild Type Parameter Screeningmentioning

confidence: 99%

Determining the role of advection in patterning by bone morphogenetic proteins through neural network model-based acceleration of a 3D finite element model of the zebrafish embryo

Wang

Chai

et al. 2022

Front. Syst. Biol.

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Embryonic development is a complex phenomenon that integrates genetic regulation and biomechanical cellular behaviors. However, the relative influence of these factors on spatiotemporal morphogen distributions is not well understood. Bone Morphogenetic Proteins (BMPs) are the primary morphogens guiding the dorsal-ventral (DV) patterning of the early zebrafish embryo, and BMP signaling is regulated by a network of extracellular and intracellular factors that impact the range and signaling of BMP ligands. Recent advances in understanding the mechanism of pattern formation support a source-sink mechanism, however, it is not clear how the source-sink mechanism shapes the morphogen patterns in three-dimensional (3D) space, nor how sensitive the pattern is to biophysical rates and boundary conditions along both the anteroposterior (AP) and DV axes of the embryo, nor how the patterns are controlled over time. Throughout blastulation and gastrulation, major cell movement, known as epiboly, happens along with the BMP-mediated DV patterning. The layer of epithelial cells begins to thin as they spread toward the vegetal pole of the embryo until it has completely engulfed the yolk cell. This dynamic domain may influence the distributions of BMP network members through advection. We developed a Finite Element Model (FEM) that incorporates all stages of zebrafish embryonic development data and solves the advection-diffusion-reaction Partial Differential Equations (PDE) in a growing domain. We use the model to investigate mechanisms in underlying BMP-driven DV patterning during epiboly. Solving the PDE is computationally expensive for parameter exploration. To overcome this obstacle, we developed a Neural Network (NN) metamodel of the 3D embryo that is accurate and fast and provided a nonlinear map between high-dimensional input and output that replaces the direct numerical simulation of the PDEs. From the modeling and acceleration by the NN metamodels, we identified the impact of advection on patterning and the influence of the dynamic expression level of regulators on the BMP signaling network.

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Section: Wild Type Parameter Screeningmentioning

confidence: 99%

Determining the role of advection in patterning by bone morphogenetic proteins through neural network model-based acceleration of a 3D finite element model of the zebrafish embryo

Wang

Chai

et al. 2022

Front. Syst. Biol.

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“…We are interested in capturing the continuous change in shape during zebrafish embryo development, particularly during epiboly, with our shape registration framework. The dataset consists of light sheet microscopy images of early stage zebrafish embryo development from [37,38]. Additionally, we analyzed the cell proliferation data from [13] to develop an accurate and physically-realistic spatial mapping between pairs of datasets of cell positions captured through in vivo imaging at different stages of the epiboly process.…”

Section: Modeling Epiboly In Zebrafish Embryosmentioning

confidence: 99%

PDE-constrained shape registration to characterize biological growth and morphogenesis from imaging data

Pawar¹,

Li²,

Gosain³

et al. 2022

Preprint

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We propose a PDE-constrained shape registration algorithm that captures the deformation and growth of biological tissue from imaging data. Shape registration is the process of evaluating optimum alignment between pairs of geometries through a spatial transformation function. We start from our previously reported work, which uses 3D tensor product B-spline basis functions to interpolate 3D space. Here, the movement of the B-spline control points, composed with an implicit function describing the shape of the tissue, yields the total deformation gradient field. The deformation gradient is then split into growth and elastic contributions. The growth tensor captures addition of mass, i.e. growth, and evolves according to a constitutive equation which is usually a function of the elastic deformation. Stress is generated in the material due to the elastic component of the deformation alone. The result of the registration is obtained by minimizing a total energy functional which includes: a distance measure reflecting similarity between the shapes, and the total elastic energy accounting for the growth of the tissue.

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“…The main reason is that 3D nuclei segmentation is easier than 3D cell segmentation, primarily because of the well-defined round shape of nuclei and the high clarity provided by nuclear dyes. Over the years, traditional algorithms such as watershed [7], wavelet [8], gradient flow tracking [9], and a multi-model approach combining more than one traditional algorithm [10] have been employed for efficient 3D nuclei segmentation. Recently, deep learning-based methods have been employed to segment 3D nuclei more accurately with specifically tailored neural network architectures [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

3DCellComposer - A Versatile Pipeline Utilizing 2D Cell Segmentation Methods for 3D Cell Segmentation

Chen,

Murphy

2024

Preprint

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Background: Cell segmentation is crucial in bioimage informatics, as its accuracy directly impacts conclusions drawn from cellular analyses. While many approaches to 2D cell segmentation have been described, 3D cell segmentation has received much less attention. 3D segmentation faces significant challenges, including limited training data availability due to the difficulty of the task for human annotators, and inherent three-dimensional complexity. As a result, existing 3D cell segmentation methods often lack broad applicability across different imaging modalities. Results: To address this, we developed a generalizable approach for using 2D cell segmentation methods to produce accurate 3D cell segmentations. We implemented this approach in 3DCellComposer, a versatile, open-source package that allows users to choose any existing 2D segmentation model appropriate for their tissue or cell type(s) without requiring any additional training. Importantly, we have enhanced our open source CellSegmentationEvaluator quality evaluation tool to support 3D images. It provides metrics that allow selection of the best approach for a given imaging source and modality, without the need for human annotations to assess performance. Using these metrics, we demonstrated that our approach produced high-quality 3D segmentations of tissue images, and that it could outperform an existing 3D segmentation method on the cell culture images with which it was trained. Conclusions: 3DCellComposer, when paired with well-trained 2D segmentation models, provides an important alternative to acquiring human-annotated 3D images for new sample types or imaging modalities and then training 3D segmentation models using them. It is expected to be of significant value for large scale projects such as the Human BioMolecular Atlas Program.

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Automatic wavelet-based 3D nuclei segmentation and analysis for multicellular embryo quantification

Cited by 8 publications

References 52 publications

Determining the role of advection in patterning by bone morphogenetic proteins through neural network model-based acceleration of a 3D finite element model of the zebrafish embryo

Determining the role of advection in patterning by bone morphogenetic proteins through neural network model-based acceleration of a 3D finite element model of the zebrafish embryo

PDE-constrained shape registration to characterize biological growth and morphogenesis from imaging data

3DCellComposer - A Versatile Pipeline Utilizing 2D Cell Segmentation Methods for 3D Cell Segmentation

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