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This process is inherently three-dimensional (3D). Yet, in part due to limitations in imaging and data analysis, cell shape analysis within tissues has largely been studied in two-dimensions (2D), e.g., the <jats:italic>Drosophila<\/jats:italic> wing disc. With recent advances in imaging and machine learning, there has been significant progress in our understanding of 3D cell and tissue shape <jats:italic>in vivo<\/jats:italic>. 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. To address the challenge of extracting 3D cell shape metrics from dense tissues, we have developed the Python package CellMet. This user-friendly tool enables extraction of quantitative shape information from 3D cell and tissue segmentations, including cell face properties, cell twist, and cell rearrangements in 3D. 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 <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/TimSaundersLab\/CellMet\" xlink:type=\"simple\">https:\/\/github.com\/TimSaundersLab\/CellMet<\/jats:ext-link>.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1013260","type":"journal-article","created":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T17:42:24Z","timestamp":1753897344000},"page":"e1013260","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":5,"title":["CellMet: Extracting 3D shape and topology metrics from confluent cells within tissues"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4206-8153","authenticated-orcid":true,"given":"Sophie","family":"Theis","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mario 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