Abstract: Deep Learning Based CT-CBCT Image Registration for Adaptive Radio Therapy

Sven Kuckertz; Nils Papenberg; Jonas Honegger; Tomasz Morgas; Benjamin Haas; Stefan Heldmann

doi:10.1007/978-3-658-29267-6_51

Abstract: Deep Learning Based CT-CBCT Image Registration for Adaptive Radio Therapy

Sven Kuckertz^*, Nils Papenberg, Jonas Honegger, Tomasz Morgas, Benjamin Haas, Stefan Heldmann

^*Corresponding author for this work

Abstract

Deformable image registration (DIR) is an important tool in radio therapy where it is used in order to align a baseline CT and daily low-dose cone beam CT (CBCT) scans. DIR allows the propagation of irradiation plans, Hounsfield units and contours of anatomical structures, respectively, which enables tracking of applied doses over time and generation of daily synthetic CT images. Furthermore, DIR allows to overcome segmentation of structures in CBCT images at each fraction.

Original language	English
Title of host publication	Bildverarbeitung für die Medizin 2020
Editors	Thomas Tolxdorff, Thomas M. Deserno, Heinz Handels, Andreas Maier, Klaus H. Maier-Hein, Christoph Palm
Number of pages	1
Publisher	Springer Vieweg, Wiesbaden
Publication date	12.02.2020
Pages	229-229
ISBN (Print)	978-3-658-29266-9
ISBN (Electronic)	978-3-658-29267-6
DOIs	https://doi.org/10.1007/978-3-658-29267-6_51
Publication status	Published - 12.02.2020
Event	Bildverarbeitung für die Medizin 2020 - International workshop on Algorithmen - Systeme - Anwendungen - Berlin, Germany Duration: 15.03.2020 → 17.03.2020 Conference number: 237969

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-658-29267-6_51

Cite this

Kuckertz, S., Papenberg, N., Honegger, J., Morgas, T., Haas, B., & Heldmann, S. (2020). Abstract: Deep Learning Based CT-CBCT Image Registration for Adaptive Radio Therapy. In T. Tolxdorff, T. M. Deserno, H. Handels, A. Maier, K. H. Maier-Hein, & C. Palm (Eds.), Bildverarbeitung für die Medizin 2020 (pp. 229-229). (Informatik aktuell ). Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-29267-6_51

Kuckertz, Sven ; Papenberg, Nils ; Honegger, Jonas et al. / Abstract: Deep Learning Based CT-CBCT Image Registration for Adaptive Radio Therapy. Bildverarbeitung für die Medizin 2020 . editor / Thomas Tolxdorff ; Thomas M. Deserno ; Heinz Handels ; Andreas Maier ; Klaus H. Maier-Hein ; Christoph Palm. Springer Vieweg, Wiesbaden, 2020. pp. 229-229 (Informatik aktuell ).

@inproceedings{b8c07f5a32324c16b4a005d3531f6879,

title = "Abstract: Deep Learning Based CT-CBCT Image Registration for Adaptive Radio Therapy",

abstract = "Deformable image registration (DIR) is an important tool in radio therapy where it is used in order to align a baseline CT and daily low-dose cone beam CT (CBCT) scans. DIR allows the propagation of irradiation plans, Hounsfield units and contours of anatomical structures, respectively, which enables tracking of applied doses over time and generation of daily synthetic CT images. Furthermore, DIR allows to overcome segmentation of structures in CBCT images at each fraction.",

author = "Sven Kuckertz and Nils Papenberg and Jonas Honegger and Tomasz Morgas and Benjamin Haas and Stefan Heldmann",

note = "Publisher Copyright: {\textcopyright} Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2020. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; Bildverarbeitung f{\"u}r die Medizin 2020 - International workshop on Algorithmen - Systeme - Anwendungen<br/> ; Conference date: 15-03-2020 Through 17-03-2020",

year = "2020",

month = feb,

day = "12",

doi = "10.1007/978-3-658-29267-6_51",

language = "English",

isbn = "978-3-658-29266-9",

series = "Informatik aktuell ",

publisher = "Springer Vieweg, Wiesbaden",

pages = "229--229",

editor = "Tolxdorff, {Thomas } and {M. Deserno}, {Thomas } and Handels, {Heinz } and Maier, {Andreas } and {H. Maier-Hein}, {Klaus } and Palm, {Christoph }",

booktitle = "Bildverarbeitung f{\"u}r die Medizin 2020",

}

Kuckertz, S, Papenberg, N, Honegger, J, Morgas, T, Haas, B & Heldmann, S 2020, Abstract: Deep Learning Based CT-CBCT Image Registration for Adaptive Radio Therapy. in T Tolxdorff, T M. Deserno, H Handels, A Maier, K H. Maier-Hein & C Palm (eds), Bildverarbeitung für die Medizin 2020 . Informatik aktuell , Springer Vieweg, Wiesbaden, pp. 229-229, Bildverarbeitung für die Medizin 2020 - International workshop on Algorithmen - Systeme - Anwendungen
, Berlin, Berlin, Germany, 15.03.20. https://doi.org/10.1007/978-3-658-29267-6_51

Abstract: Deep Learning Based CT-CBCT Image Registration for Adaptive Radio Therapy. / Kuckertz, Sven; Papenberg, Nils; Honegger, Jonas et al.
Bildverarbeitung für die Medizin 2020 . ed. / Thomas Tolxdorff; Thomas M. Deserno; Heinz Handels; Andreas Maier; Klaus H. Maier-Hein; Christoph Palm. Springer Vieweg, Wiesbaden, 2020. p. 229-229 (Informatik aktuell ).

Research output: Chapters in Books/Reports/Conference Proceedings › Conference contribution › peer-review

TY - GEN

T1 - Abstract: Deep Learning Based CT-CBCT Image Registration for Adaptive Radio Therapy

AU - Kuckertz, Sven

AU - Papenberg, Nils

AU - Honegger, Jonas

AU - Morgas, Tomasz

AU - Haas, Benjamin

AU - Heldmann, Stefan

N1 - Conference code: 237969

PY - 2020/2/12

Y1 - 2020/2/12

N2 - Deformable image registration (DIR) is an important tool in radio therapy where it is used in order to align a baseline CT and daily low-dose cone beam CT (CBCT) scans. DIR allows the propagation of irradiation plans, Hounsfield units and contours of anatomical structures, respectively, which enables tracking of applied doses over time and generation of daily synthetic CT images. Furthermore, DIR allows to overcome segmentation of structures in CBCT images at each fraction.

AB - Deformable image registration (DIR) is an important tool in radio therapy where it is used in order to align a baseline CT and daily low-dose cone beam CT (CBCT) scans. DIR allows the propagation of irradiation plans, Hounsfield units and contours of anatomical structures, respectively, which enables tracking of applied doses over time and generation of daily synthetic CT images. Furthermore, DIR allows to overcome segmentation of structures in CBCT images at each fraction.

UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85083081881&origin=inward&txGid=3eeb3edb55e46a5414e4ed2196eda4f5

U2 - 10.1007/978-3-658-29267-6_51

DO - 10.1007/978-3-658-29267-6_51

M3 - Conference contribution

SN - 978-3-658-29266-9

T3 - Informatik aktuell

SP - 229

EP - 229

BT - Bildverarbeitung für die Medizin 2020

A2 - Tolxdorff, Thomas

A2 - M. Deserno, Thomas

A2 - Handels, Heinz

A2 - Maier, Andreas

A2 - H. Maier-Hein, Klaus

A2 - Palm, Christoph

PB - Springer Vieweg, Wiesbaden

T2 - Bildverarbeitung für die Medizin 2020 - International workshop on Algorithmen - Systeme - Anwendungen<br/>

Y2 - 15 March 2020 through 17 March 2020

ER -

Kuckertz S, Papenberg N, Honegger J, Morgas T, Haas B, Heldmann S. Abstract: Deep Learning Based CT-CBCT Image Registration for Adaptive Radio Therapy. In Tolxdorff T, M. Deserno T, Handels H, Maier A, H. Maier-Hein K, Palm C, editors, Bildverarbeitung für die Medizin 2020 . Springer Vieweg, Wiesbaden. 2020. p. 229-229. (Informatik aktuell ). doi: 10.1007/978-3-658-29267-6_51

Abstract: Deep Learning Based CT-CBCT Image Registration for Adaptive Radio Therapy

Abstract

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this