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Harnessing uncertainty in domain adaptation for mri prostate lesion segmentation

Chiou, E; Giganti, F; Punwani, S; Kokkinos, I; Panagiotaki, E; (2020) Harnessing uncertainty in domain adaptation for mri prostate lesion segmentation. In: Martel, A.L and Abolmaesumi, P and Stoyanov, D and Mateus, D and Zuluaga, M.A and Zhou, S.K and Racoceanu, D and Joskowicz, L, (eds.) Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. (pp. pp. 510-520). Springer: Lima, Peru. Green open access

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Abstract

The need for training data can impede the adoption of novel imaging modalities for learning-based medical image analysis. Domain adaptation methods partially mitigate this problem by translating training data from a related source domain to a novel target domain, but typically assume that a one-to-one translation is possible. Our work addresses the challenge of adapting to a more informative target domain where multiple target samples can emerge from a single source sample. In particular we consider translating from mp-MRI to VERDICT, a richer MRI modality involving an optimized acquisition protocol for cancer characterization. We explicitly account for the inherent uncertainty of this mapping and exploit it to generate multiple outputs conditioned on a single input. Our results show that this allows us to extract systematically better image representations for the target domain, when used in tandem with both simple, CycleGAN-based baselines, as well as more powerful approaches that integrate discriminative segmentation losses and/or residual adapters. When compared to its deterministic counterparts, our approach yields substantial improvements across a broad range of dataset sizes, increasingly strong baselines, and evaluation measures.

Type: Proceedings paper
Title: Harnessing uncertainty in domain adaptation for mri prostate lesion segmentation
Event: International Conference on Medical Image Computing and Computer-Assisted Intervention
ISBN-13: 9783030597092
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-030-59710-8_50
Publisher version: https://doi.org/10.1007/978-3-030-59710-8_50
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: Domain adaptation, Image synthesis, GANs, Segmentation, MRI
UCL classification: UCL
UCL > Provost and Vice Provost Offices
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 Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10115471
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