TY - GEN
T1 - Unpaired MR Image Homogenisation by Disentangled Representations and Its Uncertainty
AU - Li, Hongwei
AU - Gopal, Sunita
AU - Sekuboyina, Anjany
AU - Zhang, Jianguo
AU - Niu, Chen
AU - Pirkl, Carolin
AU - Kirschke, Jan
AU - Wiestler, Benedikt
AU - Menze, Bjoern
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85117123024&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-87735-4_5
DO - 10.1007/978-3-030-87735-4_5
M3 - Conference contribution
AN - SCOPUS:85117123024
SN - 9783030877347
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 44
EP - 53
BT - Uncertainty 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
A2 - Sudre, Carole H.
A2 - Licandro, Roxane
A2 - Baumgartner, Christian
A2 - Melbourne, Andrew
A2 - Dalca, Adrian
A2 - Hutter, Jana
A2 - Tanno, Ryutaro
A2 - Abaci Turk, Esra
A2 - Van Leemput, Koen
A2 - Torrents Barrena, Jordina
A2 - Wells, William M.
A2 - Macgowan, Christopher
PB - Springer Science and Business Media Deutschland GmbH
T2 - 3rd 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
Y2 - 1 October 2021 through 1 October 2021
ER -