Unpaired MR Image Homogenisation by Disentangled Representations and Its Uncertainty

Hongwei Li, Sunita Gopal, Anjany Sekuboyina, Jianguo Zhang, Chen Niu, Carolin Pirkl, Jan Kirschke, Benedikt Wiestler, Bjoern Menze

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Inter-scanner and inter-protocol differences in MRI datasets are known to induce significant quantification variability. Hence data homogenisation is crucial for a reliable combination of data or observations from different sources. Existing homogenisation methods rely on pairs of images to learn a mapping from a source domain to a reference domain. In real-world, we only have access to unpaired data from the source and reference domains. In this paper, we successfully address this scenario by proposing an unsupervised image-to-image translation framework which models the complex mapping by disentangling the image space into a common content space and a scanner-specific one. We perform image quality enhancement among two MR scanners, enriching the structural information and reducing noise level. We evaluate our method on both healthy controls and multiple sclerosis (MS) cohorts and have seen both visual and quantitative improvement over state-of-the-art GAN-based methods while retaining regions of diagnostic importance such as lesions. In addition, for the first time, we quantify the uncertainty in the unsupervised homogenisation pipeline to enhance the interpretability. Codes are available: https://github.com/hongweilibran/Multi-modal-medical-image-synthesis.

Original languageEnglish
Title of host publicationUncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Placental and Preterm Image Analysis - 3rd International Workshop, UNSURE 2021, and 6th International Workshop, PIPPI 2021, Held in Conjunction with MICCAI 2021, Proceedings
EditorsCarole H. Sudre, Roxane Licandro, Christian Baumgartner, Andrew Melbourne, Adrian Dalca, Jana Hutter, Ryutaro Tanno, Esra Abaci Turk, Koen Van Leemput, Jordina Torrents Barrena, William M. Wells, Christopher Macgowan
PublisherSpringer Science and Business Media Deutschland GmbH
Pages44-53
Number of pages10
ISBN (Print)9783030877347
DOIs
StatePublished - 2021
Event3rd International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2021, and the 6th International Workshop on Perinatal, Preterm and Paediatric Image Analysis, PIPPI 2021, held in conjunction with the 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021 - Virtual, Online
Duration: 1 Oct 20211 Oct 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12959 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference3rd International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2021, and the 6th International Workshop on Perinatal, Preterm and Paediatric Image Analysis, PIPPI 2021, held in conjunction with the 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021
CityVirtual, Online
Period1/10/211/10/21

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