An end-to-end pipeline based on open source deep learning tools for reliable analysis of complex 3D images of ovaries

Autor: Manon Lesage, Manon Thomas, Thierry Pécot, Tu-Ky Ly, Nathalie Hinfray, Remy Beaudouin, Michelle Neumann, Robin Lovell-Badge, Jérôme Bugeon, Violette Thermes
Přispěvatelé: Laboratoire de Physiologie et Génomique des Poissons (LPGP), Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique )-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique ), Institut National de l'Environnement Industriel et des Risques (INERIS), Stress Environnementaux et BIOsurveillance des milieux aquatiques (SEBIO), Institut National de l'Environnement Industriel et des Risques (INERIS)-Université de Reims Champagne-Ardenne (URCA)-Université Le Havre Normandie (ULH), Normandie Université (NU)-Normandie Université (NU)-SFR Condorcet, Université de Reims Champagne-Ardenne (URCA)-Centre National de la Recherche Scientifique (CNRS)-Université de Reims Champagne-Ardenne (URCA)-Centre National de la Recherche Scientifique (CNRS), The Francis Crick Institute [London]
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Development (Cambridge, England)
Development (Cambridge, England), 2023, 150 (7), pp.dev201185. ⟨10.1242/dev.201185⟩
ISSN: 0950-1991
1477-9129
DOI: 10.1242/dev.201185⟩
Popis: Computational analysis of bio-images by deep learning (DL) algorithms has made exceptional progress in recent years and has become much more accessible to non-specialists with the development of ready-to-use tools. The study of oogenesis mechanisms and female reproductive success has also recently benefited from the development of efficient protocols for three-dimensional (3D) imaging of ovaries. Such datasets have a great potential for generating new quantitative data but are, however, complex to analyze due to the lack of efficient workflows for 3D image analysis. Here, we have integrated two existing open-source DL tools, Noise2Void and Cellpose, into an analysis pipeline dedicated to 3D follicular content analysis, which is available on Fiji. Our pipeline was developed on larvae and adult medaka ovaries but was also successfully applied to different types of ovaries (trout, zebrafish and mouse). Image enhancement, Cellpose segmentation and post-processing of labels enabled automatic and accurate quantification of these 3D images, which exhibited irregular fluorescent staining, low autofluorescence signal or heterogeneous follicles sizes. In the future, this pipeline will be useful for extensive cellular phenotyping in fish or mammals for developmental or toxicology studies.
Databáze: OpenAIRE
Externí odkaz: