Galar - a large multi-label video capsule endoscopy dataset
Abstract Video capsule endoscopy (VCE) is an important technology with many advantages (non-invasive, representation of small bowel), but faces many limitations as well (time-consuming analysis, short battery lifetime, and poor image quality). Artificial intelligence (AI) holds potential to address...
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Nature Portfolio
2025-05-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-05112-7 |
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| author | Maxime Le Floch Fabian Wolf Lucian McIntyre Christoph Weinert Albrecht Palm Konrad Volk Paul Herzog Sophie Helene Kirk Jonas L. Steinhäuser Catrein Stopp Mark Enrik Geissler Moritz Herzog Stefan Sulk Jakob Nikolas Kather Alexander Meining Alexander Hann Steffen Palm Jochen Hampe Nora Herzog Franz Brinkmann |
| author_facet | Maxime Le Floch Fabian Wolf Lucian McIntyre Christoph Weinert Albrecht Palm Konrad Volk Paul Herzog Sophie Helene Kirk Jonas L. Steinhäuser Catrein Stopp Mark Enrik Geissler Moritz Herzog Stefan Sulk Jakob Nikolas Kather Alexander Meining Alexander Hann Steffen Palm Jochen Hampe Nora Herzog Franz Brinkmann |
| author_sort | Maxime Le Floch |
| collection | DOAJ |
| description | Abstract Video capsule endoscopy (VCE) is an important technology with many advantages (non-invasive, representation of small bowel), but faces many limitations as well (time-consuming analysis, short battery lifetime, and poor image quality). Artificial intelligence (AI) holds potential to address every one of these challenges, however the progression of machine learning methods is limited by the avaibility of extensive data. We propose Galar, the most comprehensive dataset of VCE to date. Galar consists of 80 videos, culminating in 3,513,539 annotated frames covering functional, anatomical, and pathological aspects and introducing a selection of 29 distinct labels. The multisystem and multicenter VCE data from two centers in Saxony (Germany), was annotated framewise and cross-validated by five annotators. The vast scope of annotation and size of Galar make the dataset a valuable resource for the use of AI models in VCE, thereby facilitating research in diagnostic methods, patient care workflow, and the development of predictive analytics in the field. |
| format | Article |
| id | doaj-art-514f14ee99e44d81811fcf6b2ee1e897 |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-514f14ee99e44d81811fcf6b2ee1e8972025-08-20T03:48:15ZengNature PortfolioScientific Data2052-44632025-05-011211710.1038/s41597-025-05112-7Galar - a large multi-label video capsule endoscopy datasetMaxime Le Floch0Fabian Wolf1Lucian McIntyre2Christoph Weinert3Albrecht Palm4Konrad Volk5Paul Herzog6Sophie Helene Kirk7Jonas L. Steinhäuser8Catrein Stopp9Mark Enrik Geissler10Moritz Herzog11Stefan Sulk12Jakob Nikolas Kather13Alexander Meining14Alexander Hann15Steffen Palm16Jochen Hampe17Nora Herzog18Franz Brinkmann19Else Kröner Fresenius Center for Digital Health, Technische Universität Dresden (TU Dresden)Else Kröner Fresenius Center for Digital Health, Technische Universität Dresden (TU Dresden)Else Kröner Fresenius Center for Digital Health, Technische Universität Dresden (TU Dresden)Diakonissen Krankenhaus Dresden, GastroenterologyElse Kröner Fresenius Center for Digital Health, Technische Universität Dresden (TU Dresden)Else Kröner Fresenius Center for Digital Health, Technische Universität Dresden (TU Dresden)Institute of Human Genetics, Ulm University and Ulm University Medical CenterElse Kröner Fresenius Center for Digital Health, Technische Universität Dresden (TU Dresden)Else Kröner Fresenius Center for Digital Health, Technische Universität Dresden (TU Dresden)Else Kröner Fresenius Center for Digital Health, Technische Universität Dresden (TU Dresden)Department of Medicine I, University Hospital Dresden, Technische Universität Dresden (TU Dresden)Else Kröner Fresenius Center for Digital Health, Technische Universität Dresden (TU Dresden)Department of Medicine I, University Hospital Dresden, Technische Universität Dresden (TU Dresden)Else Kröner Fresenius Center for Digital Health, Technische Universität Dresden (TU Dresden)Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine II, University Hospital WürzburgInterventional and Experimental Endoscopy (InExEn), Department of Internal Medicine II, University Hospital WürzburgMedical Office for Gastroenterology and Internal MedicineElse Kröner Fresenius Center for Digital Health, Technische Universität Dresden (TU Dresden)Else Kröner Fresenius Center for Digital Health, Technische Universität Dresden (TU Dresden)Else Kröner Fresenius Center for Digital Health, Technische Universität Dresden (TU Dresden)Abstract Video capsule endoscopy (VCE) is an important technology with many advantages (non-invasive, representation of small bowel), but faces many limitations as well (time-consuming analysis, short battery lifetime, and poor image quality). Artificial intelligence (AI) holds potential to address every one of these challenges, however the progression of machine learning methods is limited by the avaibility of extensive data. We propose Galar, the most comprehensive dataset of VCE to date. Galar consists of 80 videos, culminating in 3,513,539 annotated frames covering functional, anatomical, and pathological aspects and introducing a selection of 29 distinct labels. The multisystem and multicenter VCE data from two centers in Saxony (Germany), was annotated framewise and cross-validated by five annotators. The vast scope of annotation and size of Galar make the dataset a valuable resource for the use of AI models in VCE, thereby facilitating research in diagnostic methods, patient care workflow, and the development of predictive analytics in the field.https://doi.org/10.1038/s41597-025-05112-7 |
| spellingShingle | Maxime Le Floch Fabian Wolf Lucian McIntyre Christoph Weinert Albrecht Palm Konrad Volk Paul Herzog Sophie Helene Kirk Jonas L. Steinhäuser Catrein Stopp Mark Enrik Geissler Moritz Herzog Stefan Sulk Jakob Nikolas Kather Alexander Meining Alexander Hann Steffen Palm Jochen Hampe Nora Herzog Franz Brinkmann Galar - a large multi-label video capsule endoscopy dataset Scientific Data |
| title | Galar - a large multi-label video capsule endoscopy dataset |
| title_full | Galar - a large multi-label video capsule endoscopy dataset |
| title_fullStr | Galar - a large multi-label video capsule endoscopy dataset |
| title_full_unstemmed | Galar - a large multi-label video capsule endoscopy dataset |
| title_short | Galar - a large multi-label video capsule endoscopy dataset |
| title_sort | galar a large multi label video capsule endoscopy dataset |
| url | https://doi.org/10.1038/s41597-025-05112-7 |
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