Journal
PHYSICS IN MEDICINE AND BIOLOGY
Volume 61, Issue 22, Pages 7864-7880Publisher
IOP PUBLISHING LTD
DOI: 10.1088/0031-9155/61/22/7864
Keywords
autosomal dominant kidney disease; polycystic liver disease; image segmentation; prior probability map; level set
Funding
- National Institute of Diabetes and Digestive
- Kidney Diseases of the National Institutes of Health [DK056943, DK056956, DK056957, DK056961]
- National Center for Research Resources General Clinical Research Centers at Emory University [RR000039]
- National Center for Research Resources General Clinical Research Centers at Mayo College of Medicine [RR00585]
- National Center for Research Resources General Clinical Research Centers at Kansas University Medical Center [RR23940]
- National Center for Research Resources General Clinical Research Centers at University of Alabama at Birmingham [RR000032]
- National Center for Research Resources Clinical and Translational Science Awards at Emory [RR025008]
- National Center for Research Resources Clinical and Translational Science Awards at Mayo College of Medicine [RR024150]
- National Center for Research Resources Clinical and Translational Science Awards at Kansas University Medical Center [RR033179]
- National Center for Research Resources Clinical and Translational Science Awards at University of Alabama at Birmingham [RR025777, UL1TR000165]
- National Center for Research Resources Clinical and Translational Science Awards at University of Pittsburgh School of Medicine [RR024153, UL1TR000005]
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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 tissues 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.
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