TY - JOUR
T1 - The Digital Brain Tumour Atlas, an open histopathology resource
AU - Roetzer-Pejrimovsky, Thomas
AU - Moser, Anna-Christina
AU - Atli, Baran
AU - Vogel, Clemens Christian
AU - Mercea, Petra A
AU - Prihoda, Romana
AU - Gelpi, Ellen
AU - Haberler, Christine
AU - Höftberger, Romana
AU - Hainfellner, Johannes A
AU - Baumann, Bernhard
AU - Langs, Georg
AU - Woehrer, Adelheid
N1 - Publisher Copyright:
© 2022, The Author(s).
PY - 2022/12
Y1 - 2022/12
N2 - Currently, approximately 150 different brain tumour types are defined by the WHO. Recent endeavours to exploit machine learning and deep learning methods for supporting more precise diagnostics based on the histological tumour appearance have been hampered by the relative paucity of accessible digital histopathological datasets. While freely available datasets are relatively common in many medical specialties such as radiology and genomic medicine, there is still an unmet need regarding histopathological data. Thus, we digitized a significant portion of a large dedicated brain tumour bank based at the Division of Neuropathology and Neurochemistry of the Medical University of Vienna, covering brain tumour cases from 1995-2019. A total of 3,115 slides of 126 brain tumour types (including 47 control tissue slides) have been scanned. Additionally, complementary clinical annotations have been collected for each case. In the present manuscript, we thoroughly discuss this unique dataset and make it publicly available for potential use cases in machine learning and digital image analysis, teaching and as a reference for external validation.
AB - Currently, approximately 150 different brain tumour types are defined by the WHO. Recent endeavours to exploit machine learning and deep learning methods for supporting more precise diagnostics based on the histological tumour appearance have been hampered by the relative paucity of accessible digital histopathological datasets. While freely available datasets are relatively common in many medical specialties such as radiology and genomic medicine, there is still an unmet need regarding histopathological data. Thus, we digitized a significant portion of a large dedicated brain tumour bank based at the Division of Neuropathology and Neurochemistry of the Medical University of Vienna, covering brain tumour cases from 1995-2019. A total of 3,115 slides of 126 brain tumour types (including 47 control tissue slides) have been scanned. Additionally, complementary clinical annotations have been collected for each case. In the present manuscript, we thoroughly discuss this unique dataset and make it publicly available for potential use cases in machine learning and digital image analysis, teaching and as a reference for external validation.
KW - Brain Neoplasms/diagnostic imaging
KW - Deep Learning
KW - Humans
UR - http://www.scopus.com/inward/record.url?scp=85124679463&partnerID=8YFLogxK
U2 - 10.1038/s41597-022-01157-0
DO - 10.1038/s41597-022-01157-0
M3 - Journal article
C2 - 35169150
SN - 2052-4463
VL - 9
SP - 55
JO - Scientific data
JF - Scientific data
IS - 1
M1 - 55
ER -