CellMet: Extracting 3D shape metrics from cells and tissues
Theis, S.; Mendieta-Serrano, M. A.; Chapa-Y-Lazo, B.; Chen, J.; Saunders, T. E.
Show abstract
During development and tissue repair, cells reshape and reconfigure to ensure organs take specific shapes. This process is inherently three-dimensional (3D). Yet, in part due to limitations in imaging and data analysis, cell shape analysis within tissues have been studied as a two-dimensional (2D) approximation, e.g., the Drosophila wing disc. With recent advances in imaging and machine learning, there has been significant progress in our understanding of 3D cell and tissue shape in vivo. However, even after gaining 3D segmentation of cells, it remains challenging to extract cell shape metrics beyond volume and surface area for cells within densely packed tissues. In order to extract 3D shape metrics, we have developed CellMet. This user-friendly tool enables extraction of quantitative shape information from 3D cell and tissue segmentation. It is developed for extracting cell scale information from densely packed tissues, such as cell face properties, cell twist, and cell rearrangements. Our method will improve the analysis of 3D cell shape and the understanding of cell organisation within tissues. Our tool is open source, available at https://github.com/TimSaundersLab/CellMet.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Shape-to-graph Mapping Method for Efficient Characterization and Classification of Complex Geometries in Biological Images 95%
- DeLTA: Automated cell segmentation, tracking, and lineage reconstruction using deep learning 95%
- Interkinetic nuclear movements promote apical expansion in pseudostratified epithelia at the expense of apicobasal elongation 94%
Similar papers in this journal
- Persistent homology analysis distinguishes pathological bone microstructure in non-linear microscopy images 94%
- WaveletSEG: Automatic wavelet-based 3D nuclei segmentation and analysis for multicellular embryo quantification 93%
- A Strategy to Quantify Myofibroblast Activation on a Continuous Spectrum 93%
Similar papers in this journal
- ConfluentFUCCI for fully-automated analysis of cell-cycle progression in a highly dense collective of migrating cells 96%
- Using a continuum model to decipher the mechanics of embryonic tissue spreading from time-lapse image sequences: An approximate Bayesian computation approach 95%
- Caveolae and scaffold detection from single molecule localization microscopy data using deep learning 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.