LiTS – Liver Tumor Segmentation Challenge (LiTS17)
Patrick Christ



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LITS17 (262 files)
segmentation-0.nii.zip61.06kB
segmentation-1.nii.zip58.74kB
segmentation-2.nii.zip257.34kB
segmentation-3.nii.zip279.49kB
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volume-0.nii.zip17.55MB
volume-1.nii.zip29.69MB
volume-2.nii.zip160.08MB
volume-3.nii.zip170.20MB
volume-4.nii.zip216.36MB
volume-5.nii.zip137.63MB
volume-6.nii.zip147.38MB
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volume-8.nii.zip158.36MB
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volume-10.nii.zip154.85MB
volume-11.nii.zip148.34MB
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volume-27.nii.zip247.76MB
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volume-55.nii.zip56.31MB
volume-56.nii.zip67.24MB
volume-57.nii.zip111.28MB
volume-58.nii.zip62.12MB
volume-59.nii.zip65.83MB
volume-60.nii.zip73.52MB
volume-61.nii.zip53.37MB
volume-62.nii.zip53.75MB
volume-63.nii.zip31.04MB
volume-64.nii.zip65.78MB
volume-65.nii.zip130.21MB
volume-66.nii.zip26.67MB
volume-67.nii.zip46.66MB
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Type: Dataset
Tags:

Bibtex:
@article{,
title= {LiTS – Liver Tumor Segmentation Challenge (LiTS17)},
keywords= {},
author= {Patrick Christ},
abstract= {The liver is a common site of primary (i.e. originating in the liver like hepatocellular carcinoma, HCC) or secondary (i.e. spreading to the liver like colorectal cancer) tumor development. Due to their heterogeneous and diffusive shape, automatic segmentation of tumor lesions is very challenging. Until now, only interactive methods achieved acceptable results segmenting liver lesions.

With our challenge we encourage researchers to develop 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. The training data set contains 130 CT scans and the test data set 70 CT scans. The challenge is organised in conjunction with ISBI 2017 and MICCAI 2017. For MICCAI 2017 we added tasks for liver segmentation and tumor burden estimation.

![](https://i.imgur.com/ia2qGlH.png)

![](https://i.imgur.com/eDN20ck.png)



},
terms= {},
license= {Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License},
superseded= {},
url= {https://competitions.codalab.org/competitions/17094}
}