Prostate MRI clinically significant cancer

<p>This data set is part of the public development data for&nbsp;the&nbsp;<a href="http://auc23.grand-challenge.org/">2023 Automated Universal Classification Challenge</a> (AUC23). The data set concerns clinically significant prostate cancer classification on bi-parametric magnetic resonance imaging (bpMRI) and was derived from the&nbsp;<a href="https://pi-cai.grand-challenge.org/">Prostate Imaging: Cancer AI&nbsp;(PI-CAI)</a>&nbsp;challenge&#39;s <a href="https://zenodo.org/record/6624726#.ZGI4GXZBzIU">public training and development dataset</a>&nbsp;(v2.0). The data set was originally introduced and described by <a href="https://zenodo.org/record/6522364">Saha et al. (2022)</a>,&nbsp;and no images or patient information were&nbsp;added. Data was&nbsp;restructured in compliance with the&nbsp;<a href="https://auc23.grand-challenge.org/">AUC23</a>&nbsp;challenge format.&nbsp;The data set contains 1500 anonymized prostate bpMRI scans from 1476 patients acquired between 2012-2021&nbsp;at three centers (Radboud University Medical Center, University Medical Center Groningen, Ziekenhuis Groep Twente) based in The Netherlands.</p> <p>Images are&nbsp;4D tensors:</p> <ul> <li>0: 3D Axial T2-weighted imaging (T2W)</li> <li>1: 3D Axial apparent diffusion coefficient maps&nbsp;(ADC)</li> <li>2: 3D Axial high b-value (&ge; 1000 s/mm2) diffusion-weighted imaging&nbsp;(HBV)</li> </ul> <p>Classification labels:</p> <ul> <li>0:&nbsp;Benign or indolent prostate cancer</li> <li>1: Clinically significant prostate cancer (csPCa)</li> </ul> <p>Folder structure:</p> <p>imagesTr (root folder with all patients and studies)<br> &nbsp; &nbsp; ├── 10417_1000424_0000.mha &nbsp;(axial T2W imaging for study 1000424)<br> &nbsp;&nbsp; &nbsp;├── 10417_1000424_0001.mha &nbsp;(axial ADC imaging for study 1000424)<br> &nbsp;&nbsp; &nbsp;├──&nbsp;10417_1000424_0002.mha &nbsp;(axial HBV imaging for study 1000424)<br> &nbsp;&nbsp; &nbsp;├──&nbsp;...<br> &nbsp;&nbsp; &nbsp;├── 11251_1001274_0000.mha &nbsp;(axial T2W imaging for study 1001274)<br> &nbsp;&nbsp; &nbsp;├── 11251_1001274_0001.mha &nbsp;(axial ADC imaging for study 1001274)<br> &nbsp;&nbsp; &nbsp;├── 11251_1001274_0002.mha &nbsp;(axial HBV imaging for study 1001274)<br> &nbsp; &nbsp; ├──&nbsp;...</p> <p>&nbsp;</p> <p>Please cite the following article if you are using the <a href="https://zenodo.org/record/6522364">PI-CAI: Public Training and Development Dataset</a>:</p> <pre><code>A. Saha, J. J. Twilt, J. S. Bosma, B. van Ginneken, D. Yakar, M. Elschot, J. Veltman, J. J. Fütterer, M. de Rooij, H. Huisman, "Artificial Intelligence and Radiologists at Prostate Cancer Detection in MRI: The PI-CAI Challenge (Study Protocol)", DOI: 10.5281/zenodo.6522364 </code></pre> <p>&nbsp;</p>