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

TY - CONF

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

AU - Orbes-Arteaga, Mauricio

AU - Cardoso, Manuel Jorge

AU - Sørensen, Lauge

AU - Modat, Marc

AU - Ourselin, Sebastien

AU - Nielsen, Mads

AU - Pai, Akshay Sadananda Uppinakudru

PY - 2018

Y1 - 2018

N2 - 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.

AB - 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.

M3 - Paper

T2 - 1st Conference on Medical Imaging with Deep Learning (MIDL 2018)

Y2 - 4 July 2018 through 6 July 2018

ER -