Geodesic Patch-based Segmentation
File(s)
Accepted version
Author(s)
Type
Conference Paper
Abstract
Label propagation has been shown to be effective in many automatic segmentation applications. However, its reliance on accurate image alignment means that segmentation results can be affected by any registration errors which occur. Patch-based methods relax this dependence by avoiding explicit one-to-one correspondence assumptions between images but are still limited by the search window size. Too small, and it does not account for enough registration error; too big, and it becomes more likely to select incorrect patches of similar appearance for label fusion. This paper presents a novel patch-based label propagation approach which uses relative geodesic distances to define patient-specific coordinate systems as spatial context to overcome this problem. The approach is evaluated on multi-organ segmentation of 20 cardiac MR images and 100 abdominal CT images, demonstrating competitive results.
Date Issued
2014
Date Acceptance
2014-09-14
Citation
Medical Image Computing and Computer-Assisted Intervention–MICCAI 2014: 17th International Conference, Boston, MA, USA, September 14-18, 2014, Proceedings, Part I, 2014, 8673, pp.666-673
ISBN
978-3-319-10403-4
ISSN
0302-9743
Publisher
Springer
Start Page
666
End Page
673
Journal / Book Title
Medical Image Computing and Computer-Assisted Intervention–MICCAI 2014: 17th International Conference, Boston, MA, USA, September 14-18, 2014, Proceedings, Part I
Volume
8673
Copyright Statement
The final publication is available at Springer via https://dx.doi.org/10.1007/978-3-319-10404-1_83
Source
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014
Publication Status
Published
Start Date
2014-09-14
Finish Date
2014-09-18
Coverage Spatial
Boston, MA