Joint segmentation and CT synthesis for MRI-only radiotherapy treatment planning

Ninon Burgos; Filipa Guerreiro; Jamie McClelland; Simeon Nill; David Dearnaley; Nandita Desouza; Uwe Oelfke; Antje Christin Knopf; Sï¿½bastien Ourselin; M. Jorge Cardoso

doi:10.1007/978-3-319-46723-8_63

Joint segmentation and CT synthesis for MRI-only radiotherapy treatment planning

Ninon Burgos^*, Filipa Guerreiro, Jamie McClelland, Simeon Nill, David Dearnaley, Nandita Desouza, Uwe Oelfke, Antje Christin Knopf, Sï¿½bastien Ourselin, M. Jorge Cardoso

^*Corresponding author for this work

Imaging and Biomedical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

3 Citations (Scopus)

Abstract

Accurate knowledge of organ location and tissue attenuation properties are the two essential components to perform radiotherapy treatment planning (RTP). Computed tomography (CT) has been the modality of choice for RTP as it easily provides electron density information. However,its low soft tissue contrast limits the accuracy of organ delineation. On the contrary,magnetic resonance (MR) provides images with excellent soft tissue contrast but its use for RTP is limited by the fact that it does not readily provide tissue attenuation information. In this work we propose a multi-atlas information propagation scheme that jointly segments the organs at risk and generates pseudo CT data from MR images. We demonstrate that the proposed framework is able to automatically generate accurate pseudo CT images and segmentations in the pelvic region,bypassing the need for CT scan for accurate RTP.

Original language	English
Title of host publication	Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings
Publisher	Springer Verlag
Pages	547-555
Number of pages	9
Volume	9901 LNCS
ISBN (Print)	9783319467221
DOIs	https://doi.org/10.1007/978-3-319-46723-8_63
Publication status	Published - 1 Jan 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9901 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

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10.1007/978-3-319-46723-8_63

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Burgos, N., Guerreiro, F., McClelland, J., Nill, S., Dearnaley, D., Desouza, N., Oelfke, U., Knopf, A. C., Ourselin, S., & Cardoso, M. J. (2016). Joint segmentation and CT synthesis for MRI-only radiotherapy treatment planning. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings (Vol. 9901 LNCS, pp. 547-555). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9901 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-46723-8_63

Burgos, Ninon ; Guerreiro, Filipa ; McClelland, Jamie et al. / Joint segmentation and CT synthesis for MRI-only radiotherapy treatment planning. Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. Vol. 9901 LNCS Springer Verlag, 2016. pp. 547-555 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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Burgos, N, Guerreiro, F, McClelland, J, Nill, S, Dearnaley, D, Desouza, N, Oelfke, U, Knopf, AC, Ourselin, S & Cardoso, MJ 2016, Joint segmentation and CT synthesis for MRI-only radiotherapy treatment planning. in Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. vol. 9901 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9901 LNCS, Springer Verlag, pp. 547-555. https://doi.org/10.1007/978-3-319-46723-8_63

Joint segmentation and CT synthesis for MRI-only radiotherapy treatment planning. / Burgos, Ninon; Guerreiro, Filipa; McClelland, Jamie et al.
Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. Vol. 9901 LNCS Springer Verlag, 2016. p. 547-555 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9901 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

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AU - Burgos, Ninon

AU - Guerreiro, Filipa

AU - McClelland, Jamie

AU - Nill, Simeon

AU - Dearnaley, David

AU - Desouza, Nandita

AU - Oelfke, Uwe

AU - Knopf, Antje Christin

AU - Ourselin, Sï¿½bastien

AU - Cardoso, M. Jorge

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AB - Accurate knowledge of organ location and tissue attenuation properties are the two essential components to perform radiotherapy treatment planning (RTP). Computed tomography (CT) has been the modality of choice for RTP as it easily provides electron density information. However,its low soft tissue contrast limits the accuracy of organ delineation. On the contrary,magnetic resonance (MR) provides images with excellent soft tissue contrast but its use for RTP is limited by the fact that it does not readily provide tissue attenuation information. In this work we propose a multi-atlas information propagation scheme that jointly segments the organs at risk and generates pseudo CT data from MR images. We demonstrate that the proposed framework is able to automatically generate accurate pseudo CT images and segmentations in the pelvic region,bypassing the need for CT scan for accurate RTP.

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Burgos N, Guerreiro F, McClelland J, Nill S, Dearnaley D, Desouza N et al. Joint segmentation and CT synthesis for MRI-only radiotherapy treatment planning. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings. Vol. 9901 LNCS. Springer Verlag. 2016. p. 547-555. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-46723-8_63

Joint segmentation and CT synthesis for MRI-only radiotherapy treatment planning

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