Int J Comput Assist Radiol Surg - Statistical shape model of a liver for autopsy imaging.

Tópicos

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Resumo

RPOSE: Modeling the postmortem liver for autopsy imaging is a challenging problem owing to the variation in organ deformation found in cadavers and limited availability of postmortem liver CT scans. An algorithm was developed to construct a statistical shape model (SSM) for the adult postmortem liver in autopsy imaging.METHODS: First, we investigated the relationship between SSMs obtained from in vivo liver CT scans and those from postmortem cases. Liver shapes were embedded in level set functions and statistically modeled using a spatially weighted principal components analysis. The performance of the SSMs was evaluated in terms of generalization and specificity. Several algorithms for the transformation from in vivo livers to postmortem livers were proposed to enhance the performance of an SSM for a postmortem liver, followed by a comparative study on SSMs. Specifically, five SSMs for a postmortem liver were constructed and evaluated using 32 postmortem liver labels, and postmortem liver labels synthesized from 144 in vivo liver labels were constructed using the proposed transformation algorithms. We also compared the proposed SSMs with three conventional SSMs trained from postmortem liver labels and/or in vivo liver labels.RESULTS: The investigation showed that the performance of an SSM constructed using in vivo liver labels suffered when describing postmortem liver shapes. Two of the five proposed SSMs trained using synthesized postmortem livers showed the best performance with no significant differences between them, and they statistically outperformed all conventional SSMs tested.CONCLUSIONS: The performance of conventional SSMs can be improved by using both postmortem liver shape labels and artificial shape labels synthesized from in vivo liver shape labels.

Resumo Limpo

rpose model postmortem liver autopsi imag challeng problem owe variat organ deform found cadav limit avail postmortem liver ct scan algorithm develop construct statist shape model ssm adult postmortem liver autopsi imagingmethod first investig relationship ssms obtain vivo liver ct scan postmortem case liver shape embed level set function statist model use spatial weight princip compon analysi perform ssms evalu term general specif sever algorithm transform vivo liver postmortem liver propos enhanc perform ssm postmortem liver follow compar studi ssms specif five ssms postmortem liver construct evalu use postmortem liver label postmortem liver label synthes vivo liver label construct use propos transform algorithm also compar propos ssms three convent ssms train postmortem liver label andor vivo liver labelsresult investig show perform ssm construct use vivo liver label suffer describ postmortem liver shape two five propos ssms train use synthes postmortem liver show best perform signific differ statist outperform convent ssms testedconclus perform convent ssms can improv use postmortem liver shape label artifici shape label synthes vivo liver shape label

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