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.
@article{arxiv.1806.08723,
title = {Keypoint Transfer for Fast Whole-Body Segmentation},
author = {Christian Wachinger and Matthew Toews and Georg Langs and William Wells and Polina Golland},
journal= {arXiv preprint arXiv:1806.08723},
year = {2018}
}
Comments
Accepted for publication at IEEE Transactions on Medical Imaging