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Multimodality Biomedical Image Registration Using Free Point Transformer Networks

Baum, ZMC; Hu, Y; Barratt, DC; (2020) Multimodality Biomedical Image Registration Using Free Point Transformer Networks. In: Medical Ultrasound, and Preterm, Perinatal and Paediatric Image Analysis. (pp. pp. 116-125). Springer Nature: Cham, Switzerland. Green open access

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Abstract

We describe a point-set registration algorithm based on a novel free point transformer (FPT) network, designed for points extracted from multimodal biomedical images for registration tasks, such as those frequently encountered in ultrasound-guided interventional procedures. FPT is constructed with a global feature extractor which accepts unordered source and target point-sets of variable size. The extracted features are conditioned by a shared multilayer perceptron point transformer module to predict a displacement vector for each source point, transforming it into the target space. The point transformer module assumes no vicinity or smoothness in predicting spatial transformation and, together with the global feature extractor, is trained in a data-driven fashion with an unsupervised loss function. In a multimodal registration task using prostate MR and sparsely acquired ultrasound images, FPT yields comparable or improved results over other rigid and non-rigid registration methods. This demonstrates the versatility of FPT to learn registration directly from real, clinical training data and to generalize to a challenging task, such as the interventional application presented.

Type: Proceedings paper
Title: Multimodality Biomedical Image Registration Using Free Point Transformer Networks
Event: First International Workshop, ASMUS 2020, and 5th International Workshop, PIPPI 2020, Held in Conjunction with MICCAI 2020
ISBN-13: 978-3-030-60333-5
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-030-60334-2_12
Publisher version: https://doi.org/10.1007/978-3-030-60334-2_12
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.
Keywords: Deep-learning, Point-set registration, Prostate cancer
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10113533
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