@article{discovery10134516,
         journal = {Medical Image Analysis},
            year = {2021},
           title = {Real-time multimodal image registration with partial intraoperative point-set data},
           pages = {102231},
            note = {{\copyright} 2021 The Author(s). Published by Elsevier B.V. under a Creative Commons license (https://creativecommons.org/licenses/by/4.0/).},
           month = {December},
          volume = {74},
             url = {https://doi.org/10.1016/j.media.2021.102231},
        abstract = {We present Free Point Transformer (FPT) - a deep neural network architecture for non-rigid point-set registration. Consisting of two modules, a global feature extraction module and a point transformation module, FPT does not assume explicit constraints based on point vicinity, thereby overcoming a common requirement of previous learning-based point-set registration methods. FPT is designed to accept unordered and unstructured point-sets with a variable number of points and uses a "model-free" approach without heuristic constraints. Training FPT is flexible and involves minimizing an intuitive unsupervised loss function, but supervised, semi-supervised, and partially- or weakly-supervised training are also supported. This flexibility makes FPT amenable to multimodal image registration problems where the ground-truth deformations are difficult or impossible to measure. In this paper, we demonstrate the application of FPT to non-rigid registration of prostate magnetic resonance (MR) imaging and sparsely-sampled transrectal ultrasound (TRUS) images. The registration errors were 4.71 mm and 4.81 mm for complete TRUS imaging and sparsely-sampled TRUS imaging, respectively. The results indicate superior accuracy to the alternative rigid and non-rigid registration algorithms tested and substantially lower computation time. The rapid inference possible with FPT makes it particularly suitable for applications where real-time registration is beneficial.},
          author = {Baum, ZMC and Hu, Y and Barratt, DC},
        keywords = {image-guided interventions, medical image registration, point-set registration, prostate cancer}
}