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Interactive Whole-Heart Segmentation in Congenital Heart Disease

Institution:
Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
Publisher:
Int Conf Med Image Comput Comput Assist Interv. MICCAI 2015
Publication Date:
Oct-2015
Journal:
Med Image Comput Comput Assist Interv
Volume Number:
18
Issue Number:
Pt 3
Pages:
80-8
Citation:
Int Conf Med Image Comput Comput Assist Interv. 2015 Oct;18(Pt 3):80-8.
PubMed ID:
26889498
PMCID:
PMC4753059
Appears in Collections:
NA-MIC
Sponsors:
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Pace D., Dalca A.V., Geva T., Powell A.J., Moghari M.H., Golland P. Interactive Whole-Heart Segmentation in Congenital Heart Disease. Int Conf Med Image Comput Comput Assist Interv. 2015 Oct;18(Pt 3):80-8. PMID: 26889498. PMCID: PMC4753059.
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We present an interactive algorithm to segment the heart chambers and epicardial surfaces, including the great vessel walls, in pediatric cardiac MRI of congenital heart disease. Accurate whole-heart segmentation is necessary to create patient-specific 3D heart models for surgical planning in the presence of complex heart defects. Anatomical variability due to congenital defects precludes fully automatic atlas-based segmentation. Our interactive segmentation method exploits expert segmentations of a small set of short-axis slice regions to automatically delineate the remaining volume using patch-based segmentation. We also investigate the potential of active learning to automatically solicit user input in areas where segmentation error is likely to be high. Validation is performed on four subjects with double outlet right ventricle, a severe congenital heart defect. We show that strategies asking the user to manually segment regions of interest within short-axis slices yield higher accuracy with less user input than those querying entire short-axis slices.

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