Baum, Zachary MC;
Ungi, Tamas;
Schlenger, Christopher;
Hu, Yipeng;
Barratt, Dean C;
(2022)
Learning Generalized Non-Rigid Multimodal Biomedical Image Registration from Generic Point Set Data.
In:
Proceedings of the 25th International Conference on Medical Image Computing and Computer Assisted Intervention (ASMUS: Advances in Simplifying Medical UltraSound).
(pp. pp. 1-11).
MiCCAI
(In press).
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Abstract
Free Point Transformer (FPT) has been proposed as a data-driven, non-rigid point set registration approach using deep neural networks. As FPT does not assume constraints based on point vicinity or correspondence, it may be trained simply and in a flexible manner by minimizing an unsupervised loss based on the Chamfer Distance. This makes FPT amenable to real-world medical imaging applications where ground-truth deformations may be infeasible to obtain, or in scenarios where only a varying degree of completeness in the point sets to be aligned is available. To test the limit of the correspondence finding ability of FPT and its dependency on training data sets, this work explores the generalizability of the FPT from well-curated non-medical data sets to medical imaging data sets. First, we train FPT on the ModelNet40 dataset to demonstrate its effectiveness and the superior registration performance of FPT over iterative and learning-based point set registration methods. Second, we demonstrate superior performance in rigid and non-rigid registration and robustness to missing data. Last, we highlight the interesting generalizability of the ModelNet-trained FPT by registering reconstructed freehand ultrasound scans of the spine and generic spine models without additional training, whereby the average difference to the ground truth curvatures is 1.3 degrees, across 13 patients.
| Type: | Proceedings paper |
|---|---|
| Title: | Learning Generalized Non-Rigid Multimodal Biomedical Image Registration from Generic Point Set Data |
| Event: | ASMUS 2022 - 3rd International Workshop of Advances in Simplifying Medical UltraSound (ASMUS) - held in conjunction with MICCAI 2022 |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://miccai-ultrasound.github.io/#/asmus22 |
| Language: | English |
| Additional information: | Deep-Learning, Point Sets, Registration, Scoliosis, Ultrasound |
| UCL classification: | 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 UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10153269 |
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