EPySeg: a coding-free solution for automated segmentation of epithelia using deep learning

Aigouy, Benoît; Cortes, Claudio; Liu, Shanda; Prud’homme, Benjamin

doi:10.1242/dev.194589

Cited by 40 publications

(29 citation statements)

References 23 publications

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“…First, a sub-area of the movie is duplicated to keep only the space location and timings used in the others spatiotemporal analysis of death distribution. Second, the movies of E-Cad signal is pre-treated with the machine learning based Epyseg software ( Aigouy et al., 2020 ) to improve cell-cell junctions contrast using a pre-trained model. Images where then imported to Tissue Analyser FiJi plugin ( Etournay et al., 2016 ).…”

Section: Methodsmentioning

confidence: 99%

Robustness of epithelial sealing is an emerging property of local ERK feedback driven by cell elimination

et al. 2021

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Summary What regulates the spatiotemporal distribution of cell elimination in tissues remains largely unknown. This is particularly relevant for epithelia with high rates of cell elimination where simultaneous death of neighboring cells could impair epithelial sealing. Here, using the Drosophila pupal notum (a single-layer epithelium) and a new optogenetic tool to trigger caspase activation and cell extrusion, we first showed that death of clusters of at least three cells impaired epithelial sealing; yet, such clusters were almost never observed in vivo . Accordingly, statistical analysis and simulations of cell death distribution highlighted a transient and local protective phase occurring near every cell death. This protection is driven by a transient activation of ERK in cells neighboring extruding cells, which inhibits caspase activation and prevents elimination of cells in clusters. This suggests that the robustness of epithelia with high rates of cell elimination is an emerging property of local ERK feedback.

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Section: Methodsmentioning

confidence: 99%

Robustness of epithelial sealing is an emerging property of local ERK feedback driven by cell elimination

et al. 2021

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“…Nevertheless, within the past 10-15 years, there has been significant advances in the quantitative methods used to analyse tissue dynamics [128][129][130][131][132]. Generally speaking, this appears likely to continue, in no small part due to the improvements in machine learning driven segmentation algorithms and other computational tools which will help make developmental biology more high throughput [133][134][135].…”

Section: Discussionmentioning

confidence: 99%

Active Flows and Deformable Surfaces in Development

Al-Izzi¹,

Morris²

2021

Preprint

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We review progress in active hydrodynamic descriptions of flowing media on curved and deformable manifolds: the state-of-the-art in continuum descriptions of single-layers of epithelial and/or other tissues during development. First, after a brief overview of activity, flows and hydrodynamic descriptions, we highlight the generic challenge of identifying the dependence on dynamical variables of so-called active kinetic coefficients-active counterparts to dissipative Onsager coefficients. We go on to describe some of the subtleties concerning how curvature and active flows interact, and the issues that arise when surfaces are deformable. We finish with a broad discussion around the utility of such theories in developmental biology. This includes limitations to analytical techniques, challenges associated with numerical integration, fitting-to-data and inference, and potential tools for the future, such as discrete differential geometry.

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“…TissueAnalyzer ( 23) is a tissue segmentation tool, distributed along TissueMiner ( 24) and the combination a. of these two softwares offers a framework that let end-users implement their own analyses using the R software and custom commands. EPySeg, a Python software that relies on deep-learning for the segmentation was recently made available in (25). But these tools operate on 2D images only, which implies that the epithelium is a flat plane and parallel to XY.…”

Section: Methodsmentioning

confidence: 99%

LocalZProjectorandDeProj: A toolbox for local 2D projection and accurate morphometrics of large 3D microscopy images

Herbert

Valon

Mancini

et al. 2021

Preprint

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BackgroundQuantitative imaging of epithelial tissues prompts for bioimage analysis tools that are widely applicable and accurate. In the case of imaging 3D tissues, a common post-processing step consists in projecting the acquired 3D volume on a 2D plane mapping the tissue surface. Indeed, while segmenting the tissue cells is amenable on 2D projections, it is still very difficult and cumbersome in 3D. However, for many specimen and models used in Developmental and Cell Biology, the complex content of the image volume surrounding the epithelium in a tissue often reduces the visibility of the biological object in the projection, compromising its subsequent analysis. In addition, the projection will distort the geometry of the tissue and can lead to strong artifacts in the morphology measurement.ResultsHere we introduce DProj a user-friendly tool-box built to robustly project epithelia on their 2D surface from 3D volumes, and to produce accurate morphology measurement corrected for the projection distortion, even for very curved tissues. DProj is built upon two components. LocalZProjector is a user-friendly and configurable Fiji plugin that generates 2D projections and height-maps from potentially large 3D stacks (larger than 40 GB per time-point) by only incorporating the signal of interest, despite a possibly complex image content. DeProj is a MATLAB tool that generates correct morphology measurements by combining the height-map output (such as the one offered by LocalZProjector) and the results of the cell segmentation on the 2D projection. In this paper we demonstrate DProj effectiveness over a wide range of different biological samples. We then compare its performance and accuracy against similar existing tools.ConclusionsWe find that LocalZProjector performs well even in situations where the volume to project contains spurious structures. We show that it can process large images without a pre-processing step. We study the impact of geometrical distortions on morphological measurements induced by the projection. We measured very large distortions which are then corrected by DeProj, providing accurate outputs.

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EPySeg: a coding-free solution for automated segmentation of epithelia using deep learning

Cited by 40 publications

References 23 publications

Robustness of epithelial sealing is an emerging property of local ERK feedback driven by cell elimination

Robustness of epithelial sealing is an emerging property of local ERK feedback driven by cell elimination

Active Flows and Deformable Surfaces in Development

LocalZProjectorandDeProj: A toolbox for local 2D projection and accurate morphometrics of large 3D microscopy images

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