README.md

metadata

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, arXiv).

To replicate TCGA-OV-AS, please download TCGA-OV 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

Citation

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

@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}
}