Atlas-based automated segmentation of spleen and liver using adaptive enhancement estimation.
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 = 1.09 mm; DICE/Tanimoto overlaps = 95.2/91) and liver (RMS error = 2.3 mm, and DICE/Tanimoto overlaps = 96.2/92.7). The correlations (R2) with clinical/manual height measurements were 0.97 and 0.93 for the spleen and liver respectively.
Linguraru, MG; Sandberg, JK; Li, Z; Pura, JA; Summers, RM
Medical Image Computing and Computer Assisted Intervention : Miccai ... International Conference on Medical Image Computing and Computer Assisted Intervention
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