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
Marius George
MG
Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA. lingurarum@mail.nih.gov
Sandberg
Jesse K
JK
Li
Zhixi
Z
Pura
John A
JA
Summers
Ronald M
RM
D000465:Algorithms; D003198:Computer Simulation; D021621:Imaging, Three-Dimensional / Q000379:methods; D008099:Liver / Q000000981:diagnostic imaging*; D008953:Models, Anatomic; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013154:Spleen / Q000000981:diagnostic imaging*; D013382:Subtraction Technique; D014057:Tomography, X-Ray Computed / Q000379:methods*