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Keypoint Transfer for Fast Whole-Body Segmentation

1Computer Science and Artificial Intelligence Lab (CSAIL), Massachusetts Institute of Technology (MIT), Cambridge,MA, USA.
2Ecole de Technologie Superieure, Montreal, Canada.
3Computational Imaging Research Lab, Medical University of Vienna, Austria.
4Surgical Planning Laboratory, Brigham and Women´s Hospital, Harvard Medical School, Boston, MA, USA.
IEEE Engineering in Medicine and Biology Society
Publication Date:
IEEE Trans Med Imaging
IEEE Trans Med Imaging. 2018 Jun 27.
PubMed ID:
Appears in Collections:
P41 EB015902/EB/NIBIB NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
P41 EB015898/EB/NIBIB NIH HHS/United States
Generated Citation:
Wachinger C., Toews M., Langs G., Wells W., Golland P. Keypoint Transfer for Fast Whole-Body Segmentation. IEEE Trans Med Imaging. 2018 Jun 27. PMID: 29994670. PMCID: PMC6310119.
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We introduce an approach for image segmentation based on sparse correspondences between keypoints in testing and training images. Keypoints represent automatically identified distinctive image locations, where each keypoint correspondence suggests a transformation between images. We use these correspondences to transfer label maps of entire organs from the training images to the test image. The keypoint transfer algorithm includes three steps: (i) keypoint matching, (ii) voting-based keypoint labeling, and (iii) keypoint-based probabilistic transfer of organ segmentations. We report segmentation results for abdominal organs in whole-body CT and MRI, as well as in contrast-enhanced CT and MRI. Our method offers a speed-up of about three orders of magnitude in comparison to common multi-atlas segmentation, while achieving an accuracy that compares favorably. Moreover, keypoint transfer does not require the registration to an atlas or a training phase. Finally, the method allows for the segmentation of scans with highly variable field-of-view.