Atlas-Based Automated Segmentation of Spleen and Liver Using Adaptive Enhancement Estimation [chapter]

Marius George Linguraru, Jesse K. Sandberg, Zhixi Li, John A. Pura, Ronald M. Summers
2009 Lecture Notes in Computer Science  
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
more » ... ed 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.
doi:10.1007/978-3-642-04271-3_121 pmid:20448837 pmcid:PMC2864531 fatcat:pqf3jzknjfhalnqv7coadzlapm