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Author Notes:

baek@upmc.edu

The investigators are indebted to the radiologists, nephrologists, radiology technologists, imaging engineers, and study coordinators in CRISP.

Subjects:

Research Funding:

The CRISP study is supported by cooperative agreements from the National Institute of Diabetes and Digestive and Kidney Diseases of the National Institutes of Health (DK056943, DK056956, DK056957, DK056961), by the National Center for Research Resources General Clinical Research Centers at each institution (RR000039, Emory University; RR00585, Mayo College of Medicine; RR23940, Kansas University Medical Center; RR000032, University of Alabama at Birmingham), and the National Center for Research Resources Clinical and Translational Science Awards at each institution (RR025008, Emory; RR024150, Mayo College of Medicine; RR033179, Kansas University Medical Center; RR025777 and UL1TR000165, University of Alabama at Birmingham; RR024153 and UL1TR000005, University of Pittsburgh School of Medicine).

Keywords:

  • Science & Technology
  • Technology
  • Life Sciences & Biomedicine
  • Engineering, Biomedical
  • Radiology, Nuclear Medicine & Medical Imaging
  • Engineering
  • autosomal dominant kidney disease
  • polycystic liver disease
  • image segmentation
  • prior probability map
  • level set
  • STATISTICAL SHAPE MODEL
  • PROBABILISTIC ATLAS
  • HEPATIC CYSTS
  • CT IMAGES
  • CONSTRUCTION
  • INVOLVEMENT
  • PROGRESSION
  • ALGORITHMS
  • CONSORTIUM
  • VOLUMETRY

Automated segmentation of liver and liver cysts from bounded abdominal MR images in patients with autosomal dominant polycystic kidney disease

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Journal Title:

Physics in Medicine and Biology

Volume:

Volume 61, Number 22

Publisher:

, Pages 7864-7880

Type of Work:

Article | Post-print: After Peer Review

Abstract:

Liver and liver cyst volume measurements are important quantitative imaging biomarkers for assessment of disease progression in autosomal dominant polycystic kidney disease (ADPKD) and polycystic liver disease (PLD). To date, no study has presented automated segmentation and volumetric computation of liver and liver cysts in these populations. In this paper, we proposed an automated segmentation framework for liver and liver cysts from bounded abdominal MR images in patients with ADPKD. To model the shape and variations in ADPKD livers, the spatial prior probability map (SPPM) of liver location and the tissue prior probability maps (TPPMs) of liver parenchymal tissue intensity and cyst morphology were generated. Formulated within a three-dimensional level set framework, the TPPMs successfully captured liver parenchymal tis sues and cysts, while the SPPM globally constrained the initial surfaces of the liver into the desired boundary. Liver cysts were extracted by combined operations of the TPPMs, thresholding, and false positive reduction based on spatial prior knowledge of kidney cysts and distance map. With cross-validation for the liver segmentation, the agreement between the radiology expert and the proposed method was 84% for shape congruence and 91% for volume measurement assessed by the intra-class correlation coefficient (ICC). For the liver cyst segmentation, the agreement between the reference method and the proposed method was ICC = 0.91 for cyst volumes and ICC = 0.94 for % cyst-to-liver volume.

Copyright information:

© 2016 Institute of Physics and Engineering in Medicine.

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