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Intraoperative Organ Motion Models with an Ensemble of Conditional Generative Adversarial Networks

Hu, Y; Gibson, E; Vercauteren, T; Ahmed, H; Emberton, M; Moore, C; Noble, JA; (2017) Intraoperative Organ Motion Models with an Ensemble of Conditional Generative Adversarial Networks. In: Descoteaux, M and Maier-Hein, L and Franz, A and Jannin, P and Collins, DL and Duchesne, S, (eds.) Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part II. (pp. pp. 368-376). Springer International Publishing: Cham, Switzerland. Green open access

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Abstract

In this paper, we describe how a patient-specific, ultrasound-probe-induced prostate motion model can be directly generated from a single preoperative MR image. Our motion model allows for sampling from the conditional distribution of dense displacement fields, is encoded by a generative neural network conditioned on a medical image, and accepts random noise as additional input. The generative network is trained by a minimax optimisation with a second discriminative neural network, tasked to distinguish generated samples from training motion data. In this work, we propose that (1) jointly optimising a third conditioning neural network that pre-processes the input image, can effectively extract patient-specific features for conditioning; and (2) combining multiple generative models trained separately with heuristically pre-disjointed training data sets can adequately mitigate the problem of mode collapse. Trained with diagnostic T2-weighted MR images from 143 real patients and 73,216 3D dense displacement fields from finite element simulations of intraoperative prostate motion due to transrectal ultrasound probe pressure, the proposed models produced physically-plausible patient-specific motion of prostate glands. The ability to capture biomechanically simulated motion was evaluated using two errors representing generalisability and specificity of the model. The median values, calculated from a 10-fold cross-validation, were 2.8 ± 0.3 mm and 1.7 ± 0.1 mm, respectively. We conclude that the introduced approach demonstrates the feasibility of applying state-of-the-art machine learning algorithms to generate organ motion models from patient images, and shows significant promise for future research.

Type: Proceedings paper
Title: Intraoperative Organ Motion Models with an Ensemble of Conditional Generative Adversarial Networks
Event: 20th International Conference on Medical Image Computing and Computer Assisted Intervention 2017 (MICCAI 2017)
Location: Quebec City, Canada
Dates: 10 September 2017 - 14 September 2017
ISBN-13: 9783319661858
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-319-66185-8_42
Publisher version: https://doi.org/10.1007/978-3-319-66185-8_42
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.
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 > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Surgery and Interventional Sci
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Surgery and Interventional Sci > Department of Targeted Intervention
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 Med Phys and Biomedical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/1559832
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