Abstract:
: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 the label maps of entire organs from the training images to the test image. The keypoint transfer algorithm includes three steps: 1) keypoint matching; 2) voting-based keypoint labeling; and 3) 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 with 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 a highly variable field-of-view.
journal_name
IEEE Trans Med Imagingjournal_title
IEEE transactions on medical imagingauthors
Wachinger C,Toews M,Langs G,Wells W,Golland Pdoi
10.1109/TMI.2018.2851194subject
Has Abstractpub_date
2020-02-01 00:00:00pages
273-282issue
2eissn
0278-0062issn
1558-254Xjournal_volume
39pub_type
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