量研学術機関リポジトリ「QST-Repository」は、国立研究開発法人 量子科学技術研究開発機構に所属する職員等が生み出した学術成果（学会誌発表論文、学会発表、研究開発報告書、特許等）を集積しインターネット上で広く公開するサービスです。 Welcome to QST-Repository where we accumulates and discloses the academic research results(Journal Publications, Conference presentation, Research and Development Report, Patent, etc.) of the members of National Institutes for Quantum Science and Technology.
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Purpose: 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.
\nMethods: 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.
\nResults: 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.
\nConclusions: 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.
雑誌名
International Journal of Computer Assisted Radiology and Surgery
Statistical shape model of a liver for autopsy imaging
