  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.
