IEEE Trans Med Imaging. 2020 Feb;39(2):273-282. doi: 10.1109/TMI.2018.2851194. Epub 2018 Jun 27.
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.
我们介绍了一种基于测试和训练图像中关键点之间稀疏对应关系的图像分割方法。关键点表示自动识别的图像特征位置，每个关键点对应关系提示了图像之间的变换。我们使用这些对应关系将整个器官的标签图从训练图像传输到测试图像。关键点传输算法包括三个步骤：1）关键点匹配；2）基于投票的关键点标记；3）基于关键点的器官分割概率传输。我们报告了全身 CT 和 MRI 以及对比增强 CT 和 MRI 中腹部器官的分割结果。与常见的多图谱分割相比，我们的方法速度提高了约三个数量级，同时达到了相当的准确性。此外，关键点传输不需要到图谱的配准或训练阶段。最后，该方法允许对视野变化很大的扫描进行分割。
