Keypoint Transfer Segmentation
Name
Keypoint Transfer Segmentation.pdf
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1.01 MB
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Adobe PDF
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Author(s)
Toews, M.
Wachinger, Christian
Langs, Georg
Wells, William M
Golland, Polina
Date Issued
August 2, 2017
Journal
Information Processing in Medical Imaging
Citation
Wachinger, C.; Toews, M.; Langs, G.; Wells, W. et al. “Keypoint Transfer Segmentation.” Information Processing in Medical Imaging (2015): 233–245 © Springer International Publishing Switzerland 2015
Version
Author's final manuscript
Abstract
We present an image segmentation method that transfers label maps of entire organs from the training images to the novel image to be segmented. The transfer is based on sparse correspondences between keypoints that represent automatically identified distinctive image locations. Our segmentation algorithm consists of three steps: (i) keypoint matching, (ii) voting-based keypoint labeling, and (iii) keypoint-based probabilistic transfer of organ label maps. We introduce generative models for the inference of keypoint labels and for image segmentation, where keypoint matches are treated as a latent random variable and are marginalized out as part of the algorithm. We report segmentation results for abdominal organs in whole-body CT and in contrast-enhanced CT images. The accuracy of our method compares favorably to common multi-atlas segmentation while offering a speed-up of about three orders of magnitude. Furthermore, keypoint transfer requires no training phase or registration to an atlas. The algorithm’s robustness enables the segmentation of scans with highly variable field-of-view.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1007/978-3-319-19992-4_18
