Saeed, SU;
Taylor, ZA;
Pinnock, MA;
Emberton, M;
Barratt, DC;
Hu, Y;
(2020)
Prostate Motion Modelling Using Biomechanically-Trained Deep Neural Networks on Unstructured Nodes.
In: Martel, AL and Abolmaesumi, P and Stoyanov, D and Mateus, D and Zuluaga, MA and Zhou, SK and Racoceanu, D and Joskowicz, L, (eds.)
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part IV.
(pp. pp. 650-659).
Springer: Cham, Switzerland.
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Abstract
In this paper, we propose to train deep neural networks with biomechanical simulations, to predict the prostate motion encountered during ultrasound-guided interventions. In this application, unstructured points are sampled from segmented pre-operative MR images to represent the anatomical regions of interest. The point sets are then assigned with point-specific material properties and displacement loads, forming the un-ordered input feature vectors. An adapted PointNet can be trained to predict the nodal displacements, using finite element (FE) simulations as ground-truth data. Furthermore, a versatile bootstrap aggregating mechanism is validated to accommodate the variable number of feature vectors due to different patient geometries, comprised of a training-time bootstrap sampling and a model averaging inference. This results in a fast and accurate approximation to the FE solutions without requiring subject-specific solid meshing. Based on 160,000 nonlinear FE simulations on clinical imaging data from 320 patients, we demonstrate that the trained networks generalise to unstructured point sets sampled directly from holdout patient segmentation, yielding a near real-time inference and an expected error of 0.017 mm in predicted nodal displacement.
| Type: | Proceedings paper |
|---|---|
| Title: | Prostate Motion Modelling Using Biomechanically-Trained Deep Neural Networks on Unstructured Nodes |
| Event: | MICCAI 2020: Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 |
| ISBN-13: | 978-3-030-59718-4 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/978-3-030-59719-1_63 |
| Publisher version: | https://doi.org/10.1007/978-3-030-59719-1_63 |
| 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, Biomechanical modelling, PointNet |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences 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/10113864 |
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