README.md

---
title: "TCGA-OV-AS Dataset"
license: cc-by-nc-sa-4.0
configs:
- config_name: metadata
  data_files: "metadata.csv"
---

# The Cancer Genome Atlas Ovarian Cancer for Ascites Segmentation (TCGA-OV-AS)

This dataset was curated as part of the research 'Deep Learning Segmentation of Ascites on Abdominal CT Scans for Automatic Volume Quantification' ([Paper](https://doi.org/10.1148/ryai.230601), [arXiv](https://arxiv.org/abs/2406.15979)). 

To replicate TCGA-OV-AS, please download [TCGA-OV](https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=7569497) from TCIA using the **Descriptive Directory Name** download option.

## Converting Images

Convert the DICOMs to NIFTI format using `dcm2niix` and `GNU parallel`.

1. Create the directory structure required for each NIFTI file:
   1. `find TCGA-OV -type d -exec mkdir -p -- /tmp/{} \;`
   2. `mv /tmp/TCGA-OV ./TCGA-OV-NIFTI`

2. Convert DICOMs to NIFTI
   1. `parallel --jobs $n < jobs.txt` where `$n` is number of parallel jobs.

## Ascites Dataset

285 images that are free of corruption have been hand-picked for use. The images mostly consist of **ABDOMEN-PELVIS** scans (see: `metadata.csv` for full details). 

## Clinical Information

Patient clinical data can be downloaded from TCIA: [TCGA-OV Clinical Data.zip
](https://wiki.cancerimagingarchive.net/download/attachments/7569497/TCGA-OV%20Clinical%20Data%201516.zip?version=1&modificationDate=1452105785692&api=v2)

## Citation

If you find this repository helpful in your research, please consider citing our paper:

```text
@article{hou2024deep,
  title={Deep Learning Segmentation of Ascites on Abdominal CT Scans for Automatic Volume Quantification},
  author={Hou, Benjamin and Lee, Sung-Won and Lee, Jung-Min and Koh, Christopher and Xiao, Jing and Pickhardt, Perry J. and Summers, Ronald M.}
  journal={Radiology: Artificial Intelligence},
  pages={e230601},
  year={2024},
  publisher={Radiological Society of North America}
}
```