Keypoint Transfer for Fast Whole-Body Segmentation.

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 Imaging

authors

Wachinger C,Toews M,Langs G,Wells W,Golland P

doi

10.1109/TMI.2018.2851194

subject

Has Abstract

pub_date

2020-02-01 00:00:00

pages

273-282

issue

2

eissn

0278-0062

issn

1558-254X

journal_volume

39

pub_type

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