ArticleDevelopmental biology2020
ShapeMetrics: A userfriendly pipeline for 3D cell segmentation and spatial tissue analysis.
Article in Developmental biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
11 citing papers in PubMed, 18 citations in OpenAlex.
- Biophysical simulation enables segmentation and nervous system atlas mapping for image first spatial omics.NPJ systems biology and applications · 2025Article
- Epithelial cell shape changes contribute to regulation of ureteric bud branching morphogenesis.The FEBS journal · 2025Article
- Article
- ShapeMetrics: A 3D Cell Segmentation Pipeline for Single-Cell Spatial Morphometric Analysis.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Novel image analysis tool for rapid screening of cell morphology in preclinical animal models of disease.Heliyon · 2023Article
- Fusome topology and inheritance during insect gametogenesis.PLoS computational biology · 2023Article
- Usability of deep learning pipelines for 3D nuclei identification with Stardist and Cellpose.Cells & development · 2022Article
- A novel deep learning-based 3D cell segmentation framework for future image-based disease detection.Scientific reports · 2022Article
- 3D cell morphology detection by association for embryo heart morphogenesis.Biological imaging · 2022Article
- Origami: Single-cell 3D shape dynamics oriented along the apico-basal axis of folding epithelia from fluorescence microscopy data.PLoS computational biology · 2021Article
- Red blood cell phenotyping from 3D confocal images using artificial neural networks.PLoS computational biology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
Abstract
The demand for single-cell level data is constantly increasing within life sciences. In order to meet this demand, robust cell segmentation methods that can tackle challenging in vivo tissues with complex morphology are required. However, currently available cell segmentation and volumetric analysis methods perform poorly on 3D images. Here, we generated ShapeMetrics, a MATLAB-based script that segments cells in 3D and, by performing unbiased clustering using a heatmap, separates the cells into subgroups according to their volumetric and morphological differences. The cells can be accurately segregated according to different biologically meaningful features such as cell ellipticity, longest axis, cell elongation, or the ratio between cell volume and surface area. Our machine learning based script enables dissection of a large amount of novel data from microscope images in addition to the traditional information based on fluorescent biomarkers. Furthermore, the cells in different subgroups can be spatially mapped back to their original locations in the tissue image to help elucidate their roles in their respective morphological contexts. In order to facilitate the transition from bulk analysis to single-cell level accuracy, we emphasize the user-friendliness of our method by providing detailed step-by-step instructions through the pipeline hence aiming to reach users with less experience in computational biology.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.