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+<div class="sc-jegwdG lhLRCf"><div class="sc-UEtKG dGqiYy sc-flttKd cguEtd"><div class="sc-fqwslf gsqkEc"><div class="sc-cBQMlg kAHhUk"><h2 class="sc-dcKlJK sc-cVttbi gqEuPW ksnHgj">About Dataset</h2></div></div></div><div class="sc-davvxH eCVTlP"><div class="sc-jCNfQM dTyvWO"><div style="min-height: 80px;"><div class="sc-etVRix jqYJaa sc-gVIFzB gQKGyV"><p>This dataset contains 2D image slices extracted from the publicly available Pancreas-CT-SEG dataset, which provides manually segmented pancreas annotations for contrast-enhanced 3D abdominal CT scans. The original dataset was curated by the National Institutes of Health Clinical Center (NIH) and was made available through the NCI Imaging Data Commons (IDC). The dataset consists of 82 CT scans from 53 male and 27 female subjects, converted into 2D slices for segmentation tasks.</p>
+<p>Dataset Details:</p>
+<p>Modality: Contrast-enhanced CT (portal-venous phase, ~70s post-injection)</p>
+<p>Number of Subjects: 82</p>
+<p>Age Range: 18 to 76 years (Mean: 46.8 ± 16.7 years)</p>
+<p>Scan Resolution: 512 × 512 pixels per slice</p>
+<p>Slice Thickness: Varies between 1.5 mm and 2.5 mm</p>
+<p>Scanners Used: Philips and Siemens MDCT scanners (120 kVp tube voltage)</p>
+<p>Segmentation: Manually performed by a medical student and verified by an expert radiologist</p>
+<p>Data Format: Converted from 3D DICOM/NIfTI to 2D PNG/JPEG slices for segmentation tasks</p>
+<p>Total Dataset Size: ~1.85 GB</p>
+<p>Category: Non-cancerous healthy controls (No pancreatic cancer lesions or major abdominal pathologies)</p>
+<p>Preprocessing and Conversion:</p>
+<p>The original 3D CT scans and corresponding pancreas segmentation masks (available in NIfTI format) were converted into 2D slices to facilitate 2D medical image segmentation tasks. The conversion steps include:</p>
+<p>Extracting axial slices from each 3D CT scan.</p>
+<p>Normalizing pixel intensities for consistency.</p>
+<p>Saving images in PNG/JPEG format for compatibility with deep learning frameworks.</p>
+<p>Generating corresponding binary segmentation masks where the pancreas region is labeled.</p>
+<p>Dataset Structure:</p>
+<p>Applications</p>
+<p>This dataset is ideal for medical image segmentation tasks such as:</p>
+<p>Deep learning-based pancreas segmentation (e.g., using U-Net, DeepLabV3+)</p>
+<p>Automated organ detection and localization</p>
+<p>AI-assisted diagnosis and analysis of abdominal CT scans</p>
+<p>Acknowledgments &amp; References</p>
+<p>This dataset is derived from:</p>
+<p>National Cancer Institute Imaging Data Commons (IDC) [1]</p>
+<p>The Cancer Imaging Archive (TCIA) [2]</p>
+<p>Original dataset DOI: <a rel="noreferrer nofollow" aria-label="https://doi.org/10.7937/K9/TCIA.2016.tNB1kqBU (opens in a new tab)" target="_blank" href="https://doi.org/10.7937/K9/TCIA.2016.tNB1kqBU">https://doi.org/10.7937/K9/TCIA.2016.tNB1kqBU</a></p>
+<p>Citations: If you use this dataset, please cite the following:</p>
+<p>Roth, H., Farag, A., Turkbey, E. B., Lu, L., Liu, J., &amp; Summers, R. M. (2016). Data From Pancreas-CT (Version 2). The Cancer Imaging Archive. DOI: 10.7937/K9/TCIA.2016.tNB1kqBU</p>
+<p>Fedorov, A., Longabaugh, W. J. R., Pot, D., Clunie, D. A., Pieper, S. D., Gibbs, D. L., et al. (2023). National Cancer Institute Imaging Data Commons: Toward Transparency, Reproducibility, and Scalability in Imaging Artificial Intelligence. Radiographics 43.</p>
+<p>License: This dataset is provided under the Creative Commons Attribution 4.0 International (CC-BY-4.0) license. Users must abide by the TCIA Data Usage Policy and Restrictions.</p>
+<p>Additional Resources:<br>
+Imaging Data Commons (IDC) Portal: <a rel="noreferrer nofollow" aria-label="https://portal.imaging.datacommons.cancer.gov/explore/ (opens in a new tab)" target="_blank" href="https://portal.imaging.datacommons.cancer.gov/explore/">https://portal.imaging.datacommons.cancer.gov/explore/</a></p>
+<p>OHIF DICOM Viewer: <a rel="noreferrer nofollow" aria-label="https://viewer.ohif.org/ (opens in a new tab)" target="_blank" href="https://viewer.ohif.org/">https://viewer.ohif.org/</a></p>
+<p>This dataset provides a high-quality, well-annotated resource for researchers and developers working on medical image analysis, segmentation, and AI-based pancreas detection.</p></div></div></div>
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