Keypoint Transfer for Fast Whole-Body Segmentation

TL;DR

This paper presents a fast, registration-free image segmentation method using keypoint correspondences to transfer organ labels from training to test images, significantly speeding up the process while maintaining accuracy.

Contribution

The authors introduce a novel keypoint transfer approach for whole-body segmentation that is faster and does not require registration or extensive training.

Findings

Achieves about 1000x speed-up compared to multi-atlas segmentation.

Provides accurate segmentation results comparable to traditional methods.

Handles scans with highly variable field-of-view effectively.

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.