TY  - JOUR
ID  - discovery10134516
N2  - 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.
UR  - https://doi.org/10.1016/j.media.2021.102231
JF  - Medical Image Analysis
KW  - image-guided interventions
KW  -  medical image registration
KW  -  point-set registration
KW  -  prostate cancer
A1  - Baum, ZMC
A1  - Hu, Y
A1  - Barratt, DC
TI  - Real-time multimodal image registration with partial intraoperative point-set data
VL  - 74
AV  - public
Y1  - 2021/12//
N1  - © 2021 The Author(s). Published by Elsevier B.V. under a Creative Commons license (https://creativecommons.org/licenses/by/4.0/).
ER  -