File size: 2,494 Bytes
11a4b70 2a603e0 11a4b70 b7a352e 11a4b70 b7a352e 7c85a48 b7a352e 8ae61c7 7c85a48 8ae61c7 11a4b70 69b7c4a 8ae61c7 b7a352e 7c85a48 b7a352e 7c85a48 b7a352e 9df28d0 b7a352e 9df28d0 b7a352e 8648c0c b7a352e 8648c0c b7a352e 8648c0c 7c85a48 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 |
---
license: cc-by-3.0
tags:
- medical
viewer: false
---
# The Cancer Genome Atlas Ovarian Cancer (NSCLC-Radiomics)
The models featured in this repository uses images from the publicly available [NSCLC-Radiomics](https://wiki.cancerimagingarchive.net/display/Public/NSCLC-Radiomics) Dataset.
Download the data from TCIA with **Classic Directory Name** download option.
## Converting Format
Convert DICOM images and segmentation to NIFTI format using SimpleITK, [pydicom](https://pydicom.github.io/) and [pydicom-seg](https://razorx89.github.io/pydicom-seg/guides/read.html). Run:
```shell
user@machine:~/NSCLC-Radiomics-NIFTI$ python convert.py
```
## Segmentations
Images will have one of the following segmentation files:
```
─ seg-Esophagus.nii.gz # 1
─ seg-GTV-1.nii.gz # 2
─ seg-Heart.nii.gz # 3
─ seg-Lung-Left.nii.gz # 4
─ seg-Lung-Right.nii.gz # 5
─ seg-Spinal-Cord.nii.gz # 6
```
To combine segmentations into single nifti file, run `combine_segmentations.py`.
## Requirements
```
pandas==1.5.0
pydicom==2.3.1
pydicom-seg==0.4.1
SimpleITK==2.2.0
tqdm==4.64.1
```
## Citation
If using this repository, please cite the following works:
```
Data Citation
Aerts, H. J. W. L., Wee, L., Rios Velazquez, E., Leijenaar, R. T. H., Parmar, C., Grossmann, P.,
Carvalho, S., Bussink, J., Monshouwer, R., Haibe-Kains, B., Rietveld, D., Hoebers, F.,
Rietbergen, M. M., Leemans, C. R., Dekker, A., Quackenbush, J., Gillies, R. J., Lambin, P. (2019).
Data From NSCLC-Radiomics (version 4) [Data set].
The Cancer Imaging Archive.
https://doi.org/10.7937/K9/TCIA.2015.PF0M9REI
Publication Citation
Aerts, H. J. W. L., Velazquez, E. R., Leijenaar, R. T. H., Parmar, C., Grossmann, P., Carvalho, S.,
Bussink, J., Monshouwer, R., Haibe-Kains, B., Rietveld, D., Hoebers, F., Rietbergen, M. M.,
Leemans, C. R., Dekker, A., Quackenbush, J., Gillies, R. J., Lambin, P. (2014, June 3).
Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach.
Nature Communications. Nature Publishing Group.
https://doi.org/10.1038/ncomms5006 (link)
TCIA Citation
Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M,
Tarbox L, Prior F.
The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository,
Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057.
https://doi.org/10.1007/s10278-013-9622-7
```
|