@article{hou2022segmentation,
  title={Segmentation of Ascites on Abdominal CT Scans for the Assessment of Ovarian Cancer},
  author={Hou, Benjamin and Nag, Manas K. and Lee, Jung-Min and Koh, Christopher and Summers, Ronald M.},
  journal={MedNeurIPS Workshop},
  year={2022},
  url = {https://www.cse.cuhk.edu.hk/~qdou/public/medneurips2022/97.pdf},
  abstract = {Quantification of the volume of ascites can be an accurate predictor of clinical outcomes in certain pathological settings, including ovarian cancer. Because ascites is a liquid, accurate segmentation can be challenging. This paper shows that tuning nnU-Net, a model that learns the heuristics of the data, can achieve state-of-the-art segmentation performance. The trained model achieved a Dice score of 0.66, with 0.67 precision and 0.68 recall on pathological test cases, improving on the prior state of the art.},
}