--- a
+++ b/README.md
@@ -0,0 +1,39 @@
+<div class="sc-cmRAlD dkqmWS"><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-jgvlka jFuPjz"><div class="sc-gzqKSP tNtjD"><div style="min-height: 80px;"><div class="sc-etVRix jqYJaa sc-bMmLMY ZURWJ"><h1><strong>T2-weighted Kidney MRI Segmentation Dataset</strong></h1>
+<h2><strong>A High-Quality Dataset for Kidney Segmentation in MRI</strong></h2>
+<h2>📌 <strong>Overview</strong></h2>
+<p>This dataset contains 100 T2-weighted abdominal MRI scans with manually segmented kidney masks. The MRI sequence is optimized to enhance the contrast between the kidneys and surrounding tissues, improving segmentation accuracy. It includes scans from:</p>
+<ul>
+<li>✅ Healthy control subjects</li>
+<li>✅ Chronic Kidney Disease (CKD) patients</li>
+</ul>
+<p>Additionally, 10 subjects were scanned five times in a single session to assess the precision of Total Kidney Volume (TKV) measurements.</p>
+<h2>📊 <strong>Dataset Details</strong></h2>
+<ul>
+<li>Total MRI Scans: 100</li>
+<li>Scanned Groups: Healthy &amp; CKD Patients</li>
+<li>Repetitive Scans for TKV Analysis: 10 subjects (scanned 5 times each)</li>
+<li>Data Format: NIfTI (.nii) </li>
+<li>Additional Data: Subject information in CSV file</li>
+<li>Size: 160.3 MB</li>
+<li>Source: UK Renal Imaging Network (UKRIN)</li>
+</ul>
+<h2>🔍 <strong>Use Cases</strong></h2>
+<ul>
+<li>✔️ Deep Learning &amp; AI-based Kidney Segmentation</li>
+<li>✔️ Chronic Kidney Disease (CKD) Analysis</li>
+<li>✔️ Medical Imaging Research in MRI</li>
+<li>✔️ Automated Organ Segmentation Models</li>
+</ul>
+<h2>🎈<strong>Description</strong></h2>
+<p>A dataset containing 100 T2-weighted abdominal MRI scans and manually defined kidney masks. This MRI sequence is designed to optimise contrast between the kidneys and surrounding tissue to increase the accuracy of segmentation. Half of the acquisitions were acquired of healthy control subjects while the other half were acquired from chronic kidney disease (CKD) patients. Ten of the subjects were scanned five times in the same session to enable assessment of the precision of Total Kidney Volume (TKV) measurements. More information about each subject can be found in the included csv file. This dataset was used to train a Convolutional Neural Network (CNN) to automatically segment the kidneys. </p>
+<h2>🔬 <strong>Research Potential</strong></h2>
+<p>This dataset is valuable for AI researchers, radiologists, and biomedical engineers developing CNN-based kidney segmentation models. It has been used in deep learning applications for renal segmentation and can support advancements in functional kidney imaging and CKD research.</p>
+<h2>⚖️ <strong>License</strong></h2>
+<p>CC BY 4.0 (Free to use for research and commercial applications with proper attribution)</p>
+<h2>📢 <strong>Citation</strong></h2>
+<p>If you use this dataset, please cite:<br>
+Daniel, A. J., Buchanan, C. E., Allcock, T., Scerri, D., Cox, E. F., Prestwich, B. L., &amp; Francis, S. T. (2021). T2-weighted Kidney MRI Segmentation (v1.0.0) [Data set]. Zenodo. DOI: 10.5281/zenodo.5153568</p>
+<h2>ℹ <strong>More information</strong></h2>
+<p>For more information about the dataset please refer to this article.<br>
+<a rel="noreferrer nofollow" aria-label="article (opens in a new tab)" target="_blank" href="https://onlinelibrary.wiley.com/doi/10.1002/mrm.28768">article</a></p>
+<p>For an executable that allows automated segmentation of the kidneys from this dataset please refer to this software. <a rel="noreferrer nofollow" aria-label="Renal Segmentor (opens in a new tab)" target="_blank" href="https://github.com/alexdaniel654/Renal_Segmentor">Renal Segmentor</a></p></div></div></div>=
\ No newline at end of file