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Uncertainty in Multitask Learning: Joint Representations for Probabilistic MR-only Radiotherapy Planning

Bragman, FJS; Tanno, R; Eaton-Rosen, Z; Li, W; Hawkes, DJ; Ourselin, S; Alexander, DC; ... Cardoso, MJ; + view all (2018) Uncertainty in Multitask Learning: Joint Representations for Probabilistic MR-only Radiotherapy Planning. In: Frangi, AF and Schnabel, JA and Davatzikos, C and Alberola-López, C and Fichtinger, G, (eds.) Medical Image Computing and Computer Assisted Intervention: Proceedings, Part IV. (pp. pp. 3-11). Springer: Cham, Switzerland. Green open access

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Abstract

Multi-task neural network architectures provide a mechanism that jointly integrates information from distinct sources. It is ideal in the context of MR-only radiotherapy planning as it can jointly regress a synthetic CT (synCT) scan and segment organs-at-risk (OAR) from MRI. We propose a probabilistic multi-task network that estimates: (1) intrinsic uncertainty through a heteroscedastic noise model for spatially-adaptive task loss weighting and (2) parameter uncertainty through approximate Bayesian inference. This allows sampling of multiple segmentations and synCTs that share their network representation. We test our model on prostate cancer scans and show that it produces more accurate and consistent synCTs with a better estimation in the variance of the errors, state of the art results in OAR segmentation and a methodology for quality assurance in radiotherapy treatment planning.

Type: Proceedings paper
Title: Uncertainty in Multitask Learning: Joint Representations for Probabilistic MR-only Radiotherapy Planning
Event: MICCAI 2018, 21st International Conference on Medical Image Computing and Computer Assisted Intervention, 16-20 September 2018, Granada, Spain
ISBN-13: 9783030009366
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-030-00937-3_1
Publisher version: https://doi.org/10.1007/978-3-030-00937-3_1
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
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/10059634
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