AVT: Multicenter aortic vessel tree CTA dataset collection with ground truth segmentation masks
In this article, we present a multicenter aortic vessel tree database collection, containing 56 aortas and their branches. The datasets have been acquired with computed tomography angiography (CTA) scans and each scan covers the ascending aorta, the aortic arch and its branches into the head/neck ar...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-02-01
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| Series: | Data in Brief |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340922000130 |
| _version_ | 1819279115134435328 |
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| author | Lukas Radl Yuan Jin Antonio Pepe Jianning Li Christina Gsaxner Fen-hua Zhao Jan Egger |
| author_facet | Lukas Radl Yuan Jin Antonio Pepe Jianning Li Christina Gsaxner Fen-hua Zhao Jan Egger |
| author_sort | Lukas Radl |
| collection | DOAJ |
| description | In this article, we present a multicenter aortic vessel tree database collection, containing 56 aortas and their branches. The datasets have been acquired with computed tomography angiography (CTA) scans and each scan covers the ascending aorta, the aortic arch and its branches into the head/neck area, the thoracic aorta, the abdominal aorta and the lower abdominal aorta with the iliac arteries branching into the legs. For each scan, the collection provides a semi-automatically generated segmentation mask of the aortic vessel tree (ground truth). The scans come from three different collections and various hospitals, having various resolutions, which enables studying the geometry/shape variabilities of human aortas and its branches from different geographic locations. Furthermore, creating a robust statistical model of the shape of human aortic vessel trees, which can be used for various tasks such as the development of fully-automatic segmentation algorithms for new, unseen aortic vessel tree cases, e.g. by training deep learning-based approaches. Hence, the collection can serve as an evaluation set for automatic aortic vessel tree segmentation algorithms. |
| first_indexed | 2024-12-24T00:22:46Z |
| format | Article |
| id | doaj.art-513d383256e04a14aa6f5cfd836f8b00 |
| institution | Directory Open Access Journal |
| issn | 2352-3409 |
| language | English |
| last_indexed | 2024-12-24T00:22:46Z |
| publishDate | 2022-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj.art-513d383256e04a14aa6f5cfd836f8b002022-12-21T17:24:32ZengElsevierData in Brief2352-34092022-02-0140107801AVT: Multicenter aortic vessel tree CTA dataset collection with ground truth segmentation masksLukas Radl0Yuan Jin1Antonio Pepe2Jianning Li3Christina Gsaxner4Fen-hua Zhao5Jan Egger6Graz University of Technology (TU Graz), Graz, Styria, Austria; Computer Algorithms for Medicine Laboratory (Café Lab), Graz, Styria, AustriaGraz University of Technology (TU Graz), Graz, Styria, Austria; Computer Algorithms for Medicine Laboratory (Café Lab), Graz, Styria, Austria; Research Center for Connected Healthcare Big Data, ZhejiangLab, Hangzhou, Zhejiang, 311121 ChinaGraz University of Technology (TU Graz), Graz, Styria, Austria; Computer Algorithms for Medicine Laboratory (Café Lab), Graz, Styria, AustriaGraz University of Technology (TU Graz), Graz, Styria, Austria; Computer Algorithms for Medicine Laboratory (Café Lab), Graz, Styria, Austria; Medical University of Graz (MedUni Graz), Graz, Styria, Austria; Institute for AI in Medicine (IKIM), University Hospital Essen (UKE), Ruhrgebiet, Essen, GermanyGraz University of Technology (TU Graz), Graz, Styria, Austria; Computer Algorithms for Medicine Laboratory (Café Lab), Graz, Styria, Austria; Medical University of Graz (MedUni Graz), Graz, Styria, AustriaDepartment of Radiology, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, Zhejiang, 322100 ChinaCorresponding author.; Graz University of Technology (TU Graz), Graz, Styria, Austria; Computer Algorithms for Medicine Laboratory (Café Lab), Graz, Styria, Austria; Medical University of Graz (MedUni Graz), Graz, Styria, Austria; Institute for AI in Medicine (IKIM), University Hospital Essen (UKE), Ruhrgebiet, Essen, GermanyIn this article, we present a multicenter aortic vessel tree database collection, containing 56 aortas and their branches. The datasets have been acquired with computed tomography angiography (CTA) scans and each scan covers the ascending aorta, the aortic arch and its branches into the head/neck area, the thoracic aorta, the abdominal aorta and the lower abdominal aorta with the iliac arteries branching into the legs. For each scan, the collection provides a semi-automatically generated segmentation mask of the aortic vessel tree (ground truth). The scans come from three different collections and various hospitals, having various resolutions, which enables studying the geometry/shape variabilities of human aortas and its branches from different geographic locations. Furthermore, creating a robust statistical model of the shape of human aortic vessel trees, which can be used for various tasks such as the development of fully-automatic segmentation algorithms for new, unseen aortic vessel tree cases, e.g. by training deep learning-based approaches. Hence, the collection can serve as an evaluation set for automatic aortic vessel tree segmentation algorithms.http://www.sciencedirect.com/science/article/pii/S2352340922000130AortaVessel treeCTAAortic dissectionAbdominal aortic aneurysmSegmentations |
| spellingShingle | Lukas Radl Yuan Jin Antonio Pepe Jianning Li Christina Gsaxner Fen-hua Zhao Jan Egger AVT: Multicenter aortic vessel tree CTA dataset collection with ground truth segmentation masks Data in Brief Aorta Vessel tree CTA Aortic dissection Abdominal aortic aneurysm Segmentations |
| title | AVT: Multicenter aortic vessel tree CTA dataset collection with ground truth segmentation masks |
| title_full | AVT: Multicenter aortic vessel tree CTA dataset collection with ground truth segmentation masks |
| title_fullStr | AVT: Multicenter aortic vessel tree CTA dataset collection with ground truth segmentation masks |
| title_full_unstemmed | AVT: Multicenter aortic vessel tree CTA dataset collection with ground truth segmentation masks |
| title_short | AVT: Multicenter aortic vessel tree CTA dataset collection with ground truth segmentation masks |
| title_sort | avt multicenter aortic vessel tree cta dataset collection with ground truth segmentation masks |
| topic | Aorta Vessel tree CTA Aortic dissection Abdominal aortic aneurysm Segmentations |
| url | http://www.sciencedirect.com/science/article/pii/S2352340922000130 |
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