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.