2020/02/24 by Carole J. Twining, Carole Twining, Vladimir S. Petrović +13
Computer Science · Medicine · #Advanced Neuroimaging Techniques and Applications #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #Medical Image Segmentation Techniques #cs.CV
paper · pdf · doi:10.48550/arxiv.2002.10534
10 pages, 2 Figures, 3 Tables. Submitted to IEEE Transactions on Medical Imaging
arxiv created 2020/02/24 · openalex publication_date 2020/02/24 · arxiv updated 2020/02/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We present a generic method for assessing the quality of non-rigid registration (NRR) algorithms, that does not depend on the existence of any ground truth, but depends solely on the data itself. The data is a set of images. The output of any NRR of such a set of images is a dense correspondence across the whole set. Given such a dense correspondence, it is possible to build various generative statistical models of appearance variation across the set. We show that evaluating the quality of the registration can be mapped to the problem of evaluating the quality of the resultant statistical model. The quality of the model entails a comparison between the model and the image data that was used to construct it. It should be noted that this approach does not depend on the specifics of the registration algorithm used (i.e., whether a groupwise or pairwise algorithm was used to register the set of images), or on the specifics of the modelling approach used. We derive an index of image model specificity that can be used to assess image model quality, and hence the quality of registration. This approach is validated by comparing our assessment of registration quality with that derived from ground truth anatomical labeling. We demonstrate that our approach is capable of assessing NRR reliably without ground truth. Finally, to demonstrate the practicality of our method, different NRR algorithms -- both pairwise and groupwise -- are compared in terms of their performance on 3D MR brain data.