A Model-Based Validation Scheme for Organ Segmentation in CT Scan Volumes

H. Badakhshannoory, P. Saeedi
<span title="">2011</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/nrcoa2vuhjcvfctty6zgus57um" style="color: black;">IEEE Transactions on Biomedical Engineering</a> </i> &nbsp;
In this work, we propose a novel approach for accurate 3D organ segmentation in the CT scan volumes. Instead of using the organ's prior information directly in the segmentation process, here we utilize the knowledge of the organ to validate a large number of potential segmentation outcomes that are generated by a generic segmentation process. For this, an organ space is generated based on PCA approach using which the fidelity of each segment to the organ is measured. We detail applications of
more &raquo; ... e proposed method for 3D segmentation of human kidney and liver in CT scan volumes. For evaluation, the public database of MICCAI 2007 grand challenge workshop has been incorporated. Implementation results show an average Dice similarity measure of 0.90 for segmentation of the kidney. For the liver segmentation, the proposed algorithm achieves an average volume overlap error of 8.7% and an average surface distance of 1.51 mm. Index Terms-Model based segmentation, statistical model generation, principal component analysis, model based validation
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tbme.2011.2161987">doi:10.1109/tbme.2011.2161987</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/21768040">pmid:21768040</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/32l5w3xlbzhqnhn47q3lu7svz4">fatcat:32l5w3xlbzhqnhn47q3lu7svz4</a> </span>
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