Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/133436
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Type: Conference paper
Title: Self-supervised lesion change detection and localisation in longitudinal multiple sclerosis brain imaging
Author: To, M.S.
Sarno, I.G.
Chong, C.
Jenkinson, M.
Carneiro, G.
Citation: Lecture Notes in Artificial Intelligence, 2021 / DeBruijne, M., Cattin, P.C., Cotin, S., Padoy, N., Speidel, S., Zheng, Y., Essert, C. (ed./s), vol.12907 LNCS, pp.670-680
Publisher: Springer International Publishing
Publisher Place: online
Issue Date: 2021
Series/Report no.: Lecture Notes in Computer Science
ISBN: 9783030872335
ISSN: 0302-9743
1611-3349
Conference Name: 24th International Conference on Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 (27 Sep 2021 - 1 Oct 2021 : Strasbourg)
Editor: DeBruijne, M.
Cattin, P.C.
Cotin, S.
Padoy, N.
Speidel, S.
Zheng, Y.
Essert, C.
Statement of
Responsibility: 
Minh-Son, G. Sarno, Chee Chong, Mark Jenkinson, Gustavo Carneiro
Abstract: Longitudinal imaging forms an essential component in the management and follow-up of many medical conditions. The presence of lesion changes on serial imaging can have significant impact on clinical decision making, highlighting the important role for automated change detection. Lesion changes can represent anomalies in serial imaging, which implies a limited availability of annotations and a wide variety of possible changes that need to be considered. Hence, we introduce a new unsupervised anomaly detection and localisation method trained exclusively with serial images that do not contain any lesion changes. Our training automatically synthesises lesion changes in serial images, introducing detection and localisation pseudo-labels that are used to self-supervise the training of our model. Given the rarity of these lesion changes in the synthesised images, we train the model with the imbalance robust focal Tversky loss. When compared to supervised models trained on different datasets, our method shows competitive performance in the detection and localisation of new demyelinating lesions on longitudinal magnetic resonance imaging in multiple sclerosis patients. Code for the models will be made available at https://github.com/toson87/MSChangeDetection.
Keywords: Change detection; Siamese networks; multiple sclerosis
Rights: © Springer Nature Switzerland AG 2021
DOI: 10.1007/978-3-030-87234-2_63
Grant ID: http://purl.org/au-research/grants/arc/DP180103232
http://purl.org/au-research/grants/arc/FT190100525
Published version: https://link.springer.com/book/10.1007/978-3-030-87234-2
Appears in Collections:Computer Science publications

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