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

Publikation: KonferencebidragPaperForskning

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: KonferencebidragPaperForskning

Harvard

Orbes-Arteaga, M, Cardoso, MJ, Sørensen, L, Modat, M, Ourselin, S, Nielsen, M & Pai, ASU 2018, 'Simultaneous synthesis of FLAIR and segmentation of white matter hypointensities from T1 MRIs', Paper fremlagt ved 1st Conference on Medical Imaging with Deep Learning (MIDL 2018), Amsterdam, Holland, 04/07/2018 - 06/07/2018.

APA

Orbes-Arteaga, M., Cardoso, M. J., Sørensen, L., Modat, M., Ourselin, S., Nielsen, M., & Pai, A. S. U. (2018). Simultaneous synthesis of FLAIR and segmentation of white matter hypointensities from T1 MRIs. Paper præsenteret ved 1st Conference on Medical Imaging with Deep Learning (MIDL 2018), Amsterdam, Holland.

Vancouver

Orbes-Arteaga M, Cardoso MJ, Sørensen L, Modat M, Ourselin S, Nielsen M o.a.. Simultaneous synthesis of FLAIR and segmentation of white matter hypointensities from T1 MRIs. 2018. Paper præsenteret ved 1st Conference on Medical Imaging with Deep Learning (MIDL 2018), Amsterdam, Holland.

Author

Orbes-Arteaga, Mauricio ; Cardoso, Manuel Jorge ; Sørensen, Lauge ; Modat, Marc ; Ourselin, Sebastien ; Nielsen, Mads ; Pai, Akshay Sadananda Uppinakudru. / Simultaneous synthesis of FLAIR and segmentation of white matter hypointensities from T1 MRIs. Paper præsenteret ved 1st Conference on Medical Imaging with Deep Learning (MIDL 2018), Amsterdam, Holland.8 s.

Bibtex

@conference{ab022d2bf79d4e27a41fa62f24a13081,
title = "Simultaneous synthesis of FLAIR and segmentation of white matter hypointensities from T1 MRIs",
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.",
author = "Mauricio Orbes-Arteaga and Cardoso, {Manuel Jorge} and Lauge S{\o}rensen and Marc Modat and Sebastien Ourselin and Mads Nielsen and Pai, {Akshay Sadananda Uppinakudru}",
year = "2018",
language = "English",
note = "1st Conference on Medical Imaging with Deep Learning (MIDL 2018) ; Conference date: 04-07-2018 Through 06-07-2018",

}

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