Simultaneous synthesis of FLAIR and segmentation of white matter hypointensities from T1 MRIs
Publikation: Konferencebidrag › Paper › Forskning
Standard
Simultaneous synthesis of FLAIR and segmentation of white matter hypointensities from T1 MRIs. / Orbes-Arteaga, Mauricio; Cardoso, Manuel Jorge; Sørensen, Lauge; Modat, Marc; Ourselin, Sebastien; Nielsen, Mads; Pai, Akshay Sadananda Uppinakudru.
2018. Paper præsenteret ved 1st Conference on Medical Imaging with Deep Learning (MIDL 2018), Amsterdam, Holland.Publikation: Konferencebidrag › Paper › Forskning
Harvard
APA
Vancouver
Author
Bibtex
}
RIS
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 -
ID: 199025688