Int J Comput Assist Radiol Surg - Capturing large shape variations of liver using population-based statistical shape models.

Tópicos

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Resumo

RPOSE: 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.

Resumo Limpo

rpose statist shape model ssms repres morpholog variat specif object larg shape variat shape paramet constitut larg space may includ incorrect paramet human liver nonrigid organ subject larg deform due extern forc bodi posit chang scan procedur develop test populationbas model repres shape livermethod upper abdomin ctscan input imag repres convent shape model shape paramet individu liver extract ct scan employ classifi differ popul correspond popul ssm model built liver surfac paramet space divid sever subspac compact origin space propos model test use ctscan liver imag data set method evalu model compact reconstruct error general specif measuresresult propos model implement test use ct scan includ liver shape larg shape variat method compar convent recent develop shape model method accuraci propos model near twice achiev convent model propos populationbas model general compar convent model mean reconstruct error propos model mm convent model mmconclus populationbas model repres shape liver develop test favor result use approach liver shape ct scan model compact general accur model

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