Document Detail


Atlas-based automated segmentation of spleen and liver using adaptive enhancement estimation.
MedLine Citation:
PMID:  20426209     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
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.
Authors:
Marius George Linguraru; Jesse K Sandberg; Zhixi Li; John A Pura; Ronald M Summers
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Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Intramural    
Journal Detail:
Title:  Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention     Volume:  12     ISSN:  -     ISO Abbreviation:  Med Image Comput Comput Assist Interv     Publication Date:  2009  
Date Detail:
Created Date:  2010-04-29     Completed Date:  2010-06-07     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101249582     Medline TA:  Med Image Comput Comput Assist Interv     Country:  Germany    
Other Details:
Languages:  eng     Pagination:  1001-8     Citation Subset:  IM    
Affiliation:
Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA. lingurarum@mail.nih.gov
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MeSH Terms
Descriptor/Qualifier:
Algorithms
Computer Simulation
Imaging, Three-Dimensional / methods
Liver / radiography*
Models, Anatomic*
Pattern Recognition, Automated / methods*
Radiographic Image Enhancement / methods*
Radiographic Image Interpretation, Computer-Assisted / methods*
Reproducibility of Results
Sensitivity and Specificity
Spleen / radiography*
Subtraction Technique
Tomography, X-Ray Computed / methods*

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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