title: Atlas-based Automated Segmentation of Spleen and Liver using Adaptive Enhancement Estimation.
abstract: The paper presents the automated segmentation of spleen and liver from contrast-enhanced CT images of normal and hepato/splenomegaly populations. The method used 4 steps: (i) a mean organ model was registered to the patient CT; (ii) the first estimates of the organs were improved by a geodesic active contour; (iii) the contrast enhancements of liver and spleen were estimated to adjust to patient image characteristics, and an adaptive convolution refined the segmentations; (iv) lastly, a normalized probabilistic atlas corrected for shape and location for the precise computation of each organ's volume and height (mid-hepatic liver height and cephalocaudal spleen height). Results from test data demonstrated the method's ability to accurately segment the spleen (RMS error = 1.09mm; DICE/Tanimoto overlaps = 95.2/91) and liver (RMS error = 2.3mm, and DICE/Tanimoto overlaps = 96.2/92.7). The correlations (R(2)) with clinical/manual height measurements were 0.97 and 0.93 for the spleen and liver respectively.
authors.forename: Marius George
authors.initials: MG
authors.lastname: Linguraru
authors.forename: Jesse K
authors.initials: JK
authors.lastname: Sandberg
authors.forename: Zhixi
authors.initials: Z
authors.lastname: Li
authors.forename: John A
authors.initials: JA
authors.lastname: Pura
authors.forename: Ronald M
authors.initials: RM
authors.lastname: Summers
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