An open-source platform for structured annotation and computational workflows in digital pathology research
Abstract The rapid evolution of digital pathology has enabled large-scale data acquisition, driving sophisticated clinical research and advancing the development of AI-driven tools. These innovations have also revolutionised histopathological slide review, especially the annotation step (i.e. the pr...
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Nature Portfolio
2025-08-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-13546-7 |
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| author | Luca Lianas Mauro Del Rio Luca Pireddu Oskar Aspegren Francesca Giunchi Michelangelo Fiorentino Simone Leo Renata Zelic Per Henrik Vincent Nicolas Destefanis Daniela Zugna Lorenzo Richiardi Andreas Pettersson Olof Akre Francesca Frexia |
| author_facet | Luca Lianas Mauro Del Rio Luca Pireddu Oskar Aspegren Francesca Giunchi Michelangelo Fiorentino Simone Leo Renata Zelic Per Henrik Vincent Nicolas Destefanis Daniela Zugna Lorenzo Richiardi Andreas Pettersson Olof Akre Francesca Frexia |
| author_sort | Luca Lianas |
| collection | DOAJ |
| description | Abstract The rapid evolution of digital pathology has enabled large-scale data acquisition, driving sophisticated clinical research and advancing the development of AI-driven tools. These innovations have also revolutionised histopathological slide review, especially the annotation step (i.e. the process of marking specific areas of interest on glass-mounted tissue samples to add relevant clinical information) by digitising the process, enhancing precision and efficiency, and facilitating collaboration. However, currently available open-source annotation tools typically employ single-label approaches that provide a flat representation of whole-slide images (WSI), limiting their ability to capture the complexity of the diagnosis-significant elements in a detailed and structured way. Furthermore, the difficulty of strictly following precise review protocols and lack of provenance tracking during annotation processes can result in high variability and limit reproducibility and reusability of the collected data. In this work we present the CRS4 Digital Pathology Platform (CDPP), an open-source system for research studies that manages WSI collections and focuses on high-quality, structured annotations, gathered according to well-defined protocols. Its main features include: (1) structured, multi-label morphological and clinical image annotation; (2) support for controlled but customisable annotation protocols; (3) dedicated annotation tools to facilitate enhanced accuracy, efficiency and consistency in the annotation process; and (4) workflow-based computational analysis with integrated provenance tracking. We show how the platform has successfully supported three different studies, demonstrating the CDPP’s ability to assist pathologists in the generation of high-quality annotated datasets, also suitable for reuse, in the context of digital pathology research. |
| format | Article |
| id | doaj-art-78098bf13c014e9a9f7df34a77846ea8 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-78098bf13c014e9a9f7df34a77846ea82025-08-20T03:45:49ZengNature PortfolioScientific Reports2045-23222025-08-0115111510.1038/s41598-025-13546-7An open-source platform for structured annotation and computational workflows in digital pathology researchLuca Lianas0Mauro Del Rio1Luca Pireddu2Oskar Aspegren3Francesca Giunchi4Michelangelo Fiorentino5Simone Leo6Renata Zelic7Per Henrik Vincent8Nicolas Destefanis9Daniela Zugna10Lorenzo Richiardi11Andreas Pettersson12Olof Akre13Francesca Frexia14Visual and Data-intensive Computing, CRS4 (Center for Advanced Studies, Research and Development in Sardinia)Visual and Data-intensive Computing, CRS4 (Center for Advanced Studies, Research and Development in Sardinia)Visual and Data-intensive Computing, CRS4 (Center for Advanced Studies, Research and Development in Sardinia)Department of Pathology and Cancer Diagnostics, Karolinska University HospitalDepartment of Pathology, IRCCS Azienda Ospedaliero-Universitaria di BolognaDepartment of Medical and Surgical Sciences, University of BolognaVisual and Data-intensive Computing, CRS4 (Center for Advanced Studies, Research and Development in Sardinia)Department of Molecular Medicine and Surgery, Karolinska InstitutetDepartment of Molecular Medicine and Surgery, Karolinska InstitutetDepartment of Medical Sciences, University of TurinDepartment of Medical Sciences, University of TurinDepartment of Medical Sciences, University of TurinDivision of Clinical Epidemiology, Department of Medicine Solna, Karolinska InstitutetDepartment of Molecular Medicine and Surgery, Karolinska InstitutetVisual and Data-intensive Computing, CRS4 (Center for Advanced Studies, Research and Development in Sardinia)Abstract The rapid evolution of digital pathology has enabled large-scale data acquisition, driving sophisticated clinical research and advancing the development of AI-driven tools. These innovations have also revolutionised histopathological slide review, especially the annotation step (i.e. the process of marking specific areas of interest on glass-mounted tissue samples to add relevant clinical information) by digitising the process, enhancing precision and efficiency, and facilitating collaboration. However, currently available open-source annotation tools typically employ single-label approaches that provide a flat representation of whole-slide images (WSI), limiting their ability to capture the complexity of the diagnosis-significant elements in a detailed and structured way. Furthermore, the difficulty of strictly following precise review protocols and lack of provenance tracking during annotation processes can result in high variability and limit reproducibility and reusability of the collected data. In this work we present the CRS4 Digital Pathology Platform (CDPP), an open-source system for research studies that manages WSI collections and focuses on high-quality, structured annotations, gathered according to well-defined protocols. Its main features include: (1) structured, multi-label morphological and clinical image annotation; (2) support for controlled but customisable annotation protocols; (3) dedicated annotation tools to facilitate enhanced accuracy, efficiency and consistency in the annotation process; and (4) workflow-based computational analysis with integrated provenance tracking. We show how the platform has successfully supported three different studies, demonstrating the CDPP’s ability to assist pathologists in the generation of high-quality annotated datasets, also suitable for reuse, in the context of digital pathology research.https://doi.org/10.1038/s41598-025-13546-7Digital PathologyComputational PathologyHistopathologyWSI AnnotationAnnotation ProtocolComputational Provenance |
| spellingShingle | Luca Lianas Mauro Del Rio Luca Pireddu Oskar Aspegren Francesca Giunchi Michelangelo Fiorentino Simone Leo Renata Zelic Per Henrik Vincent Nicolas Destefanis Daniela Zugna Lorenzo Richiardi Andreas Pettersson Olof Akre Francesca Frexia An open-source platform for structured annotation and computational workflows in digital pathology research Scientific Reports Digital Pathology Computational Pathology Histopathology WSI Annotation Annotation Protocol Computational Provenance |
| title | An open-source platform for structured annotation and computational workflows in digital pathology research |
| title_full | An open-source platform for structured annotation and computational workflows in digital pathology research |
| title_fullStr | An open-source platform for structured annotation and computational workflows in digital pathology research |
| title_full_unstemmed | An open-source platform for structured annotation and computational workflows in digital pathology research |
| title_short | An open-source platform for structured annotation and computational workflows in digital pathology research |
| title_sort | open source platform for structured annotation and computational workflows in digital pathology research |
| topic | Digital Pathology Computational Pathology Histopathology WSI Annotation Annotation Protocol Computational Provenance |
| url | https://doi.org/10.1038/s41598-025-13546-7 |
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