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Epithelial fluid transport properties in PDOs can be measured through visual changes in organoid (lumen) size. Such organoid phenotypes have been highly instrumental for the studying of diseases, including cystic fibrosis (CF), which is characterized by genetic mutations of the CF transmembrane conductance regulator (CFTR) ion channel. Here we present OrgaSegment, a MASK-RCNN based deep-learning segmentation model allowing for the segmentation of individual intestinal PDO structures from bright-field images. OrgaSegment recognizes spherical structures in addition to the oddly-shaped organoids that are a hallmark of CF organoids and can be used in organoid swelling assays, including the new drug-induced swelling assay that we show here. OrgaSegment enabled easy quantification of organoid swelling and could discriminate between organoids with different CFTR mutations, as well as measure responses to CFTR modulating drugs. The easy-to-apply label-free segmentation tool can help to study CFTR-based fluid secretion and possibly other epithelial ion transport mechanisms in organoids.<\/jats:p>","DOI":"10.1038\/s42003-024-05966-4","type":"journal-article","created":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T12:02:12Z","timestamp":1710331332000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":51,"title":["OrgaSegment: deep-learning based organoid segmentation to quantify CFTR dependent fluid secretion"],"prefix":"10.1038","volume":"7","author":[{"given":"Juliet W.","family":"Lefferts","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Suzanne","family":"Kroes","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthew B.","family":"Smith","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paul J.","family":"Niem\u00f6ller","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Natascha D. 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OrgaSegment: deep-learning based organoid segmentation to quantify CFTR-dependent fluid secretion [dataset] Zenodo https:\/\/doi.org\/10.5281\/zenodo.10278229.","DOI":"10.5281\/zenodo.10278229"},{"key":"5966_CR37","doi-asserted-by":"publisher","unstructured":"Lefferts, J. et al. OrgaSegment: deep-learning based organoid segmentation to quantify CFTR-dependent fluid secretion [dataset] Zenodo https:\/\/doi.org\/10.5281\/zenodo.10610437.","DOI":"10.5281\/zenodo.10610437"},{"key":"5966_CR38","doi-asserted-by":"publisher","unstructured":"Lefferts, J. et al. OrgaSegment: deep-learning based organoid segmentation to quantify CFTR-dependent fluid secretion [code] Zenodo https:\/\/doi.org\/10.5281\/zenodo.7886026.","DOI":"10.5281\/zenodo.7886026"},{"key":"5966_CR39","doi-asserted-by":"publisher","unstructured":"Lefferts, J. et al. 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C.K.v.d.E. report grants from Eloxx, Galapagos NV, Gilead, GSK, Nutricia, ProQR, Proteostasis, Teva, and Vertex Pharmaceuticals Incorporated, and a patent (10006904) with royalties paid. All other authors declare no competing interests. All other authors have nothing to disclose.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"We declare that the biobank donors gave their informed consent for inclusion before donation. All experiments performed in this manuscript are performed according the this informed consent. The study was conducted in accordance with the Declaration of Helsinki, and the biobank inclusion protocol was approved by the Biobank Ethics Committee of the University Medical Centre Utrecht under protocol numbers 14-008 and 19-831.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}],"article-number":"319"}}