Capturing large shape variations of liver using population-based statistical shape models

Purpose Statistical shape models (SSMs) represent morphological variations of a specific object. When there are large shape variations, the shape parameters constitute a large space that may include incorrect parameters. The human liver is a non-rigid organ subject to large deformations due to exter...

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Veröffentlicht in:International journal for computer assisted radiology and surgery Jg. 9; H. 6; S. 967 - 977
Hauptverfasser: Foruzan, Amir H., Chen, Yen-Wei, Hori, Masatoshi, Sato, Yoshinobu, Tomiyama, Noriyuki
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2014
Schlagworte:
Algorithms
Computer Imaging
Computer Science
Health Informatics
Humans
Imaging
Imaging, Three-Dimensional
Liver - diagnostic imaging
Liver Neoplasms - diagnostic imaging
Medicine
Medicine & Public Health
Models, Statistical
Original Article
Pattern Recognition and Graphics
Radiology
Reproducibility of Results
Sensitivity and Specificity
Surgery
Tomography, X-Ray Computed - methods
Vision
Statistical shape model
Population-based shape model
Shape representation
Medical image analysis
ISSN:1861-6410, 1861-6429, 1861-6429
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Zusammenfassung:Purpose Statistical shape models (SSMs) represent morphological variations of a specific object. When there are large shape variations, the shape parameters constitute a large space that may include incorrect parameters. The human liver is a non-rigid organ subject to large deformations due to external forces or body position changes during scanning procedures. We developed and tested a population-based model to represent the shape of liver. Methods Upper abdominal CT-scan input images are represented by a conventional shape model. The shape parameters of individual livers extracted from the CT scans are employed to classify them into different populations. Corresponding to each population, an SSM model is built. The liver surface parameter space is divided into several subspaces which are more compact than the original space. The proposed model was tested using 29 CT-scan liver image data sets. The method was evaluated by model compactness, reconstruction error, generality and specificity measures. Results The proposed model is implemented and tested using CT scans that included liver shapes with large shape variations. The method was compared with conventional and recently developed shape modeling methods. The accuracy of the proposed model was nearly twice that achieved with the conventional model. The proposed population-based model was more general compared with the conventional model. The mean reconstruction error of the proposed model was 0.029 mm while that of the conventional model was 0.052 mm. Conclusion A population-based model to represent the shape of liver was developed and tested with favorable results. Using this approach, the liver shapes from CT scans were modeled by a more compact, more general, and more accurate model.
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ISSN:1861-6410
1861-6429
1861-6429
DOI:10.1007/s11548-014-1000-5