Surgical Planning Laboratory - Brigham & Women's Hospital - Boston, Massachusetts USA - a teaching affiliate of Harvard Medical School

Surgical Planning Laboratory

The Publication Database hosted by SPL

All Publications | Upload | Advanced Search | Gallery View | Download Statistics | Help | Import | Log in

ShapeCut: Bayesian Surface Estimation using Shape-driven Graph

1Scientific Computing and Imaging (SCI) Institute, University of Utah, Salt Lake City, UT, USA. Electronic address:
2Faculty of Computers and Information, Cairo University, Egypt.
Elsevier Science
Publication Date:
Med Image Anal
Volume Number:
Med Image Anal. 2017 Aug;40:11-29.
PubMed ID:
Atrial fibrillation, Bayesian segmentation, Geometric graph, Graph-cuts, Mesh generation, Parametric shape priors
Appears in Collections:
P41 GM103545/GM/NIGMS NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Veni G., Elhabian S.Y., Whitaker R.T. ShapeCut: Bayesian Surface Estimation using Shape-driven Graph. Med Image Anal. 2017 Aug;40:11-29. PMID: 28582702. PMCID: PMC5546629.
Downloaded: 287 times. [view map]
Paper: Download, View online
Export citation:
Google Scholar: link

A variety of medical image segmentation problems present significant technical challenges, including heterogeneous pixel intensities, noisy/ill-defined boundaries and irregular shapes with high variability. The strategy of estimating optimal segmentations within a statistical framework that combines image data with priors on anatomical structures promises to address some of these technical challenges. However, methods that rely on local optimization techniques and/or local shape penalties (e.g., smoothness) have been proven to be inadequate for many difficult segmentation problems. These challenging segmentation problems can benefit from the inclusion of global shape priors within a maximum-a-posteriori estimation framework, which biases solutions toward an object class of interest. In this paper, we propose a maximum-a-posteriori formulation that relies on a generative image model by incorporating both local and global shape priors. The proposed method relies on graph cuts as well as a new shape parameters estimation that provides a global updates-based optimization strategy. We demonstrate our approach on synthetic datasets as well as on the left atrial wall segmentation from late-gadolinium enhancement MRI, which has been shown to be effective for identifying myocardial fibrosis in the diagnosis of atrial fibrillation. Experimental results prove the effectiveness of the proposed approach in terms of the average surface distance between extracted surfaces and the corresponding ground-truth, as well as the clinical efficacy of the method in the identification of fibrosis and scars in the atrial wall.