Deep Learning Segmentation of Ascites on Abdominal CT Scans for Automatic Volume Quantification
<|reference_start|>Deep Learning Segmentation of Ascites on Abdominal CT Scans for Automatic Volume Quantification: Purpose: To evaluate the performance of an automated deep learning method in detecting ascites and subsequently quantifying its volume in patients with liver cirrhosis and ovarian cancer.   Materials and Methods: This retrospective study included contrast-enhanced and non-contrast abdominal-pelvic CT scans of patients with cirrhotic ascites and patients with ovarian cancer from two institutions, National Institutes of Health (NIH) and University of Wisconsin (UofW). The model, trained on The Cancer Genome Atlas Ovarian Cancer dataset (mean age, 60 years +/- 11 [s.d.]; 143 female), was tested on two internal (NIH-LC and NIH-OV) and one external dataset (UofW-LC). Its performance was measured by the Dice coefficient, standard deviations, and 95% confidence intervals, focusing on ascites volume in the peritoneal cavity.   Results: On NIH-LC (25 patients; mean age, 59 years +/- 14 [s.d.]; 14 male) and NIH-OV (166 patients; mean age, 65 years +/- 9 [s.d.]; all female), the model achieved Dice scores of 0.855 +/- 0.061 (CI: 0.831-0.878) and 0.826 +/- 0.153 (CI: 0.764-0.887), with median volume estimation errors of 19.6% (IQR: 13.2-29.0) and 5.3% (IQR: 2.4-9.7) respectively. On UofW-LC (124 patients; mean age, 46 years +/- 12 [s.d.]; 73 female), the model had a Dice score of 0.830 +/- 0.107 (CI: 0.798-0.863) and median volume estimation error of 9.7% (IQR: 4.5-15.1). The model showed strong agreement with expert assessments, with r^2 values of 0.79, 0.98, and 0.97 across the test sets.   Conclusion: The proposed deep learning method performed well in segmenting and quantifying the volume of ascites in concordance with expert radiologist assessments.<|reference_end|>
@article{hou2024deep,
    title={Deep Learning Segmentation of Ascites on Abdominal CT Scans for
  Automatic Volume Quantification},
    author={Benjamin Hou, Sung-Won Lee, Jung-Min Lee, Christopher Koh, Jing Xiao,
  Perry J. Pickhardt, Ronald M. Summers},
    journal={arXiv preprint arXiv:2406.15979},
    year={2024},
    doi={10.1148/ryai.230601},
    archivePrefix={arXiv},
    eprint={2406.15979},
    primaryClass={eess.IV cs.CV}
}