Dataset of prostate MRI annotated for anatomical zones and cancer
Lisa C. Adams,
Marcus R. Makowski,
Günther Engel,
Maximilian Rattunde,
Felix Busch,
Patrick Asbach,
Stefan M. Niehues,
Shankeeth Vinayahalingam,
Bram van Ginneken,
Geert Litjens,
Keno K. Bressem
Affiliations
Lisa C. Adams
Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Institute for Radiology, Hindenburgdamm 30, 12203 Berlin, Germany; Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany
Marcus R. Makowski
Technical University of Munich, Department of Diagnostic and Interventional Radiology, Faculty of Medicine, Ismaninger Str. 22, 81675, Munich, Germany
Günther Engel
Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Institute for Radiology, Hindenburgdamm 30, 12203 Berlin, Germany; Institute for Diagnostic and Interventional Radiology, Georg-August University, Göttingen, Germany
Maximilian Rattunde
Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Institute for Radiology, Hindenburgdamm 30, 12203 Berlin, Germany
Felix Busch
Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Institute for Radiology, Hindenburgdamm 30, 12203 Berlin, Germany
Patrick Asbach
Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Institute for Radiology, Hindenburgdamm 30, 12203 Berlin, Germany
Stefan M. Niehues
Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Institute for Radiology, Hindenburgdamm 30, 12203 Berlin, Germany
Shankeeth Vinayahalingam
Radboud University Medical Center, Nijmegen, GA, The Netherlands
Bram van Ginneken
Radboud University Medical Center, Nijmegen, GA, The Netherlands
Geert Litjens
Radboud University Medical Center, Nijmegen, GA, The Netherlands
Keno K. Bressem
Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Institute for Radiology, Hindenburgdamm 30, 12203 Berlin, Germany; Corresponding author: Dr. Keno Bressem, Department of Radiology, Charité University, Hindenburgdamm 30, 12203 Berlin, Germany.
In the present work, we present a publicly available, expert-segmented representative dataset of 158 3.0 Tesla biparametric MRIs [1]. There is an increasing number of studies investigating prostate and prostate carcinoma segmentation using deep learning (DL) with 3D architectures [2–7]. The development of robust and data-driven DL models for prostate segmentation and assessment is currently limited by the availability of openly available expert-annotated datasets [8–10].The dataset contains 3.0 Tesla MRI images of the prostate of patients with suspected prostate cancer. Patients over 50 years of age who had a 3.0 Tesla MRI scan of the prostate that met PI-RADS version 2.1 technical standards were included. All patients received a subsequent biopsy or surgery so that the MRI diagnosis could be verified/matched with the histopathologic diagnosis. For patients who had undergone multiple MRIs, the last MRI, which was less than six months before biopsy/surgery, was included. All patients were examined at a German university hospital (Charité Universitätsmedizin Berlin) between 02/2016 and 01/2020. All MRI were acquired with two 3.0 Tesla MRI scanners (Siemens VIDA and Skyra, Siemens Healthineers, Erlangen, Germany). Axial T2W sequences and axial diffusion-weighted sequences (DWI) with apparent diffusion coefficient maps (ADC) were included in the data set.T2W sequences and ADC maps were annotated by two board-certified radiologists with 6 and 8 years of experience, respectively. For T2W sequences, the central gland (central zone and transitional zone) and peripheral zone were segmented. If areas of suspected prostate cancer (PIRADS score of ≥ 4) were identified on examination, they were segmented in both the T2W sequences and ADC maps.Because restricted diffusion is best seen in DWI images with high b-values, only these images were selected and all images with low b-values were discarded. Data were then anonymized and converted to NIfTI (Neuroimaging Informatics Technology Initiative) format.
