Dataset of prostate MRI annotated for anatomical zones and cancer

In the present work, we present a publicly available, expert -segmented representative dataset of 158 3.0 Tesla biparamet-ric MRIs [1] . There is an increasing number of studies inves-tigating prostate and prostate carcinoma segmentation using deep learning (DL) with 3D architectures [2-7] . The develop-ment of robust and data-driven DL models for prostate seg-mentation and assessment is currently limited by the avail-ability 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 in-cluded. 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 ex-amined at a German university hospital (Charite Univer-sitatsmedizin 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 seg-mented. 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 im-ages with low b-values were discarded. Data were then anonymized and converted to NIfTI (Neuroimaging Informat-ics Technology Initiative) format.(c) 2022 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)