@article{,
title= {NIH Pancreas-CT Dataset},
keywords= {},
journal= {},
author= {Holger R. Roth and Amal Farag and Evrim B. Turkbey and Le Lu and Jiamin Liu and Ronald M. Summers. },
year= {},
url= {http://doi.org/10.7937/K9/TCIA.2016.tNB1kqBU},
license= {Creative Commons Attribution 3.0 Unported License},
abstract= {### Summary

The National Institutes of Health Clinical Center performed 82 abdominal contrast enhanced 3D CT scans (~70 seconds after intravenous contrast injection in portal-venous) from 53 male and 27 female subjects.  Seventeen of the subjects are healthy kidney donors scanned prior to nephrectomy.  The remaining 65 patients were selected by a radiologist from patients who neither had major abdominal pathologies nor pancreatic cancer lesions.  Subjects' ages range from 18 to 76 years with a mean age of 46.8 ± 16.7. The CT scans have resolutions of 512x512 pixels with varying pixel sizes and slice thickness between 1.5 − 2.5 mm, acquired on Philips and Siemens MDCT scanners (120 kVp tube voltage).

A medical student manually performed slice-by-slice segmentations of the pancreas as ground-truth and these were verified/modified by an experienced radiologist.

The images were processed into nii files using the following script:

```
for i in `ls . | grep PAN`; do 
   echo $i; 
   dcm2niix -vox 1 -z y -o ./data/ -m y -s y -f %n $i
done
```

### Citation

Roth HR, Lu L, Farag A, Shin H-C, Liu J, Turkbey EB, Summers RM. DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation. N. Navab et al. (Eds.): MICCAI 2015, Part I, LNCS 9349, pp. 556–564, 2015. 

### Examples

![](https://i.imgur.com/4aZNgw6.gifv)

![](https://i.imgur.com/kfhhH7x.png)

![](https://i.imgur.com/kGbz9hl.png)

},
superseded= {},
terms= {}
}