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Simultaneous synthesis of FLAIR and segmentation of white matter hypointensities from T1 MRIs

Research output: Contribution to journalArticle

Mauricio Orbes-Arteaga, M. Jorge Cardoso, Lauge Sørensen, Marc Modat, Sébastien Ourselin, Mads Nielsen, Akshay Pai

Original languageEnglish
Journal arXiv
Publication statusPublished - 20 Aug 2018

Bibliographical note

Conference on Medical Imaging with Deep Learning MIDL 2018

Documents

  • 1808.06519v1

    1808.06519v1.pdf, 298 KB, application/pdf

    9/10/2018

King's Authors

Abstract

Segmenting vascular pathologies such as white matter lesions in Brain magnetic resonance images (MRIs) require acquisition of multiple sequences such as T1-weighted (T1-w) --on which lesions appear hypointense-- and fluid attenuated inversion recovery (FLAIR) sequence --where lesions appear hyperintense--. However, most of the existing retrospective datasets do not consist of FLAIR sequences. Existing missing modality imputation methods separate the process of imputation, and the process of segmentation. In this paper, we propose a method to link both modality imputation and segmentation using convolutional neural networks. We show that by jointly optimizing the imputation network and the segmentation network, the method not only produces more realistic synthetic FLAIR images from T1-w images, but also improves the segmentation of WMH from T1-w images only.

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