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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 ikRdXB"><div style="min-height: 80px;"><div class="sc-etVRix jqYJaa sc-gVIFzB gQKGyV"><h1>Abstract</h1>
+<blockquote>
+  <p>130 CT scans for segmentation of the liver as well as tumor lesions.</p>
+</blockquote>
+<h1>About this dataset</h1>
+<blockquote>
+  <p>Liver cancer is the <strong>fifth</strong> most commonly occurring cancer in men and the <strong>ninth</strong> most commonly occurring cancer in women. There were over <strong>840,000 new cases in 2018</strong>.</p>
+  <p>The liver is a common site of primary or secondary tumor development. Due to their heterogeneous and diffusive shape, automatic segmentation of tumor lesions is very challenging.</p>
+  <p>In light of that, we encourage the development of automatic segmentation algorithms to segment liver lesions in contrast­-enhanced abdominal CT scans. The data and segmentations are provided by various clinical sites around the world.<br>
+  This dataset was extracted from LiTS – Liver Tumor Segmentation Challenge (LiTS17) organised in conjunction with ISBI 2017 and MICCAI 2017.</p>
+</blockquote>
+<h1>How to use</h1>
+<blockquote>
+  <ul>
+  <li>Create a segmentation model for segmenting liver and/or liver tumor lesions.</li>
+  <li>Your kernel can be featured here!</li>
+  <li><a aria-label="Dataset Part 2 (opens in a new tab)" target="_blank" href="https://www.kaggle.com/andrewmvd/liver-tumor-segmentation-part-2">Dataset Part 2</a></li>
+  <li><a rel="noreferrer nofollow" aria-label="Dataset article (opens in a new tab)" target="_blank" href="https://arxiv.org/abs/1901.04056">Dataset article</a></li>
+  <li><a aria-label="More Datasets (opens in a new tab)" target="_blank" href="https://www.kaggle.com/andrewmvd/datasets">More Datasets</a></li>
+  </ul>
+</blockquote>
+<h1>Acknowledgements</h1>
+<p>If you use this dataset in your research, please credit the authors.</p>
+<blockquote>
+  <h3>Splash banner</h3>
+  <p>Image by ©yodiyim</p>
+  <h3>Splash icon</h3>
+  <p>Icon made by Freepik available on <a rel="noreferrer nofollow" aria-label="www.flaticon.com (opens in a new tab)" target="_blank" href="https://www.flaticon.com/">www.flaticon.com</a>.</p>
+  <h3>License</h3>
+  <p>CC BY NC ND 4.0</p>
+  <h3>BibTeX</h3>
+  <p><a aria-label="@misc (opens in a new tab)" target="_blank" href="https://www.kaggle.com/misc" data-id="3e9872ad-9ac0-4608-bc2b-ed4b5fd5c73d" data-user-name="misc" class="user-mention">@misc</a>{bilic2019liver,<br>
+      title={The Liver Tumor Segmentation Benchmark (LiTS)},<br>
+      author={Patrick Bilic and Patrick Ferdinand Christ and Eugene Vorontsov and Grzegorz Chlebus and Hao Chen and Qi Dou and Chi-Wing Fu and Xiao Han and Pheng-Ann Heng and Jürgen Hesser and Samuel Kadoury and Tomasz Konopczynski and Miao Le and Chunming Li and Xiaomeng Li and Jana Lipkovà and John Lowengrub and Hans Meine and Jan Hendrik Moltz and Chris Pal and Marie Piraud and Xiaojuan Qi and Jin Qi and Markus Rempfler and Karsten Roth and Andrea Schenk and Anjany Sekuboyina and Eugene Vorontsov and Ping Zhou and Christian Hülsemeyer and Marcel Beetz and Florian Ettlinger and Felix Gruen and Georgios Kaissis and Fabian Lohöfer and Rickmer Braren and Julian Holch and Felix Hofmann and Wieland Sommer and Volker Heinemann and Colin Jacobs and Gabriel Efrain Humpire Mamani and Bram van Ginneken and Gabriel Chartrand and An Tang and Michal Drozdzal and Avi Ben-Cohen and Eyal Klang and Marianne M. Amitai and Eli Konen and Hayit Greenspan and Johan Moreau and Alexandre Hostettler and Luc Soler and Refael Vivanti and Adi Szeskin and Naama Lev-Cohain and Jacob Sosna and Leo Joskowicz and Bjoern H. Menze},<br>
+      year={2019},<br>
+      eprint={1901.04056},<br>
+      archivePrefix={arXiv},<br>
+      primaryClass={cs.CV}<br>
+  }</p>
+</blockquote></div></div></div>
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