Chapter title |
Globally Optimal Label Fusion with Shape Priors
|
---|---|
Chapter number | 62 |
Book title |
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016
|
Published in |
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, October 2016
|
DOI | 10.1007/978-3-319-46723-8_62 |
Pubmed ID | |
Book ISBNs |
978-3-31-946722-1, 978-3-31-946723-8
|
Authors |
Ipek Oguz, Satyananda Kashyap, Hongzhi Wang, Paul Yushkevich, Milan Sonka |
Abstract |
Multi-atlas label fusion methods have gained popularity in a variety of segmentation tasks given their attractive performance. Graph-based segmentation methods are widely used given their global optimality guarantee. We propose a novel approach, GOLF, that combines the strengths of these two approaches. GOLF incorporates shape priors to the label-fusion problem and provides a globally optimal solution even for the multi-label scenario, while also leveraging the highly accurate posterior maps from a multi-atlas label fusion approach. We demonstrate GOLF for the joint segmentation of the left and right pairs of caudate, putamen, globus pallidus and nucleus accumbens. Compared to the FreeSurfer and FIRST approaches, GOLF is significantly more accurate on all reported indices for all 8 structures. We also present comparisons to a multi-atlas approach, which reveals further insights on the contributions of the different components of the proposed framework. |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Unknown | 11 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Researcher | 3 | 27% |
Student > Ph. D. Student | 2 | 18% |
Student > Postgraduate | 2 | 18% |
Professor > Associate Professor | 1 | 9% |
Student > Master | 1 | 9% |
Other | 0 | 0% |
Unknown | 2 | 18% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 2 | 18% |
Medicine and Dentistry | 2 | 18% |
Sports and Recreations | 1 | 9% |
Neuroscience | 1 | 9% |
Engineering | 1 | 9% |
Other | 0 | 0% |
Unknown | 4 | 36% |