Deep Learning Segmentation of Ascites on Abdominal CT Scans for Automatic Volume Quantification.

Journal: Radiology. Artificial intelligence
PMID: 38900043

Abstract

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 patients with ovarian cancer. Materials and Methods This retrospective study included contrast-enhanced and noncontrast 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 [±SD], 60 years ± 11; 143 female), was tested on two internal datasets (NIH-LC and NIH-OV) and one external dataset (UofW-LC). Its performance was measured by the F1/Dice coefficient, SDs, and 95% CIs, focusing on ascites volume in the peritoneal cavity. Results On NIH-LC (25 patients; mean age, 59 years ± 14; 14 male) and NIH-OV (166 patients; mean age, 65 years ± 9; all female), the model achieved F1/Dice scores of 85.5% ± 6.1 (95% CI: 83.1, 87.8) and 82.6% ± 15.3 (95% CI: 76.4, 88.7), 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; 73 female), the model had a F1/Dice score of 83.0% ± 10.7 (95% CI: 79.8, 86.3) and median volume estimation error of 9.7% (IQR, 4.5%-15.1%). The model showed strong agreement with expert assessments, with 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 patients with cirrhosis and those with ovarian cancer, in concordance with expert radiologist assessments. Abdomen/GI, Cirrhosis, Deep Learning, Segmentation . © RSNA, 2024 See also commentary by Aisen and Rodrigues in this issue.

Authors

  • Benjamin Hou
    Biomedical Image Analysis Group (BioMedIA), Imperial College London, London, UK.
  • Sungwon Lee
    Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bldg. 10 Room 1C224D MSC 1182, Bethesda, MD 20892-1182.
  • Jung-Min Lee
    From the Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 10 Center Dr, Bldg 10, Rm 1C224, Bethesda, MD 20892-1182 (B.H., R.M.S.); Department of Radiology, The Catholic University of Korea, Seoul St. Mary's Hospital, Seoul, Korea (S.L.); Women's Malignancies Branch, National Cancer Institute, National Institutes of Health, Bethesda, Md (J.M.L.); Liver Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Md (C.K.); Ping An Technology, Shenzhen, China (J.X.); and Department of Radiology, University of Wisconsin School of Medicine & Public Health, Madison, Wis (P.J.P.).
  • Christopher Koh
    Translational Hepatology Unit Liver Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health Bethesda MD.
  • Jing Xiao
    Xiyuan Hospital, China Academy of Chinese Medical Sciences(CACMS), Beijing, China.
  • Perry J Pickhardt
    University of Wisconsin Medical School, Department of Radiology, Madison, Wisconsin, United States.
  • Ronald M Summers
    National Institutes of Health, Clinical Center, Radiology and Imaging Sciences, 10 Center Drive, Bethesda, MD 20892, USA.

Keywords

External Resources

Popular Topics
Recent Journals