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Pace DF, Dalca AV, Geva T, et al. Interactive Whole-Heart Segmentation in Congenital Heart Disease. Med Image Comput Comput Assist Interv. 2015;9351:80-88doi: 10.1007/978-3-319-24574-4_10.
Pace, D. F., Dalca, A. V., Geva, T., Powell, A. J., Moghari, M. H., & Golland, P. (2015). Interactive Whole-Heart Segmentation in Congenital Heart Disease. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 935180-88. https://doi.org/10.1007/978-3-319-24574-4_10
Pace, Danielle F, et al. "Interactive Whole-Heart Segmentation in Congenital Heart Disease." Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention vol. 9351 (2015): 80-88. doi: https://doi.org/10.1007/978-3-319-24574-4_10
Pace DF, Dalca AV, Geva T, Powell AJ, Moghari MH, Golland P. Interactive Whole-Heart Segmentation in Congenital Heart Disease. Med Image Comput Comput Assist Interv. 2015 Oct;9351:80-88. doi: 10.1007/978-3-319-24574-4_10. Epub 2015 Nov 18. PMID: 26889498; PMCID: PMC4753059.
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Med Image Comput Comput Assist Interv. 2015 Oct;9351:80-88. doi: 10.1007/978-3-319-24574-4_10. Epub 2015 Nov 18.

Interactive Whole-Heart Segmentation in Congenital Heart Disease.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention

Danielle F Pace, Adrian V Dalca, Tal Geva, Andrew J Powell, Mehdi H Moghari, Polina Golland

Affiliations

  1. Computer Science and Artificial Intelligence Lab, MIT, Cambridge, MA, USA.
  2. Department of Cardiology, Boston Children's Hospital, Boston, MA, USA; Department of Pediatrics, Harvard Medical School, Boston, MA, USA.

PMID: 26889498 PMCID: PMC4753059 DOI: 10.1007/978-3-319-24574-4_10

Abstract

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.

References

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