Keypoint Transfer for Fast Whole-Body Segmentation

Citation:

Christian Wachinger, Matthew Toews, Georg Langs, William Wells, and Polina Golland. 2020. “Keypoint Transfer for Fast Whole-Body Segmentation.” IEEE Trans Med Imaging, 39, 2, Pp. 273-82. Copy at http://www.tinyurl.com/y7sf7z8w

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 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.
Last updated on 07/13/2020