Saeed, SU, Taylor, ZA orcid.org/0000-0002-0718-1663, Pinnock, MA et al. (3 more authors) (2020) Prostate Motion Modelling Using Biomechanically-Trained Deep Neural Networks on Unstructured Nodes. In: Lecture Notes in Computer Science. MICCAI 2020: Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 04-08 Oct 2020, Lima, Peru. Springer Verlag , pp. 650-659. ISBN 9783030597184
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.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © Springer Nature Switzerland AG 2020. This is an author produced version of a conference paper published in Lecture Notes for Computer Science. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Deep learning; Biomechanical modelling; PointNet |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mechanical Engineering (Leeds) > Institute of Medical and Biological Engineering (iMBE) (Leeds) |
Funding Information: | Funder Grant number Cancer Research UK A28832 |
Depositing User: | Symplectic Publications |
Date Deposited: | 25 Nov 2020 13:32 |
Last Modified: | 25 Nov 2020 13:32 |
Status: | Published |
Publisher: | Springer Verlag |
Identification Number: | 10.1007/978-3-030-59719-1_63 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:168318 |