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Fully Automatic 3D Segmentation of Iceball for Image-guided Cryoablation

Institution:
Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. xinyang@bwh.harvard.edu
Publisher:
IEEE Engineering in Medicine and Biology Society EMBS 2012
Publication Date:
Aug-2012
Journal:
Conf Proc IEEE Eng Med Biol Soc
Volume Number:
2012
Pages:
2327-30
Citation:
Conf Proc IEEE Eng Med Biol Soc. 2012 Aug;2012:2327-30.
PubMed ID:
23366390
PMCID:
PMC3663297
Keywords:
Image segmentation, shape, tumors
Appears in Collections:
NCIGT, SLICER, SPL
Sponsors:
P41 EB015898/EB/NIBIB NIH HHS/United States
P41 RR019703/RR/NCRR NIH HHS/United States
R01 CA152282/CA/NCI NIH HHS/United States
Generated Citation:
Liu X., Tuncali K., Wells III W.M., Morrison P.R., Zientara G.P. Fully Automatic 3D Segmentation of Iceball for Image-guided Cryoablation. Conf Proc IEEE Eng Med Biol Soc. 2012 Aug;2012:2327-30. PMID: 23366390. PMCID: PMC3663297.
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The efficient extraction of the cryoablation iceball from a time series of 3D images is crucial during cryoablation to assist the interventionalist in determining the coverage of the tumor by the ablated volume. Conventional semi-automatic segmentation tools such as ITK-SNAP and 3D Slicer's Fast Marching Segmentation can attain accurate iceball segmentation in retrospective studies, however, they are not ideal for intraprocedure real time segmentation, as they require time-consuming manual operations, such as the input of fiducials and the extent of the segmented region growth. In this paper, we present an innovative approach for the segmentation of the iceball during cryoablation, that executes a fully automatic computation. Our approach is based on the graph cuts segmentation framework, and incorporates prior information of iceball shape evolving in time, modeled using experimentally-derived iceball growth parameters. Modeling yields a shape prior mask image at each timepoint of the imaging time series for use in the segmentation. Segmentation results of our method and the ITK-SNAP method are compared for 8 timepoints in 2 cases. The results indicate that our fully automatic approach is accurate, robust and highly efficient compared to manual and semi-automatic approaches.

Additional Material
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LiuX-EMBS2012-fig1.jpg (152.521kB)