Automatic deep learning based quality assessment of transperineal ultrasound guided prostate radiotherapy

Saskia M. Camps; Tim Houben; Christopher Edwards; Maria Antico; Matteo Dunnhofer; Esther Martens; Jose Baeza; Ben G.L. Vanneste; Evert Van Limbergen; Peter H.N. de With; Frank J.W. Verhaegen; Gustavo Carneiro; D. Fontanarosa

doi:10.1002/jmrs.324

Automatic deep learning based quality assessment of transperineal ultrasound guided prostate radiotherapy

Saskia M. Camps
, Tim Houben
, Christopher Edwards
, Maria Antico
, Matteo Dunnhofer
, Esther Martens
, Jose Baeza
, Ben G.L. Vanneste
, Evert Van Limbergen
, Peter H.N. de With
, Frank J.W. Verhaegen
, Gustavo Carneiro
, D. Fontanarosa

Research output: Contribution to journal › Conference article › peer-review

Abstract

Ultrasound (US) is one of the imaging modalities that can be used for image‐guided radiotherapy (RT) workflows of prostate cancer patients. It allows real‐time volumetric tracking during the course of the RT treatment, which could potentially improve the precision of radiation dose delivery. However, intra‐fraction motion management using US image guidance is not yet widespread. This can be partially attributed to the need for image interpretation by a trained operator during or after US image acquisition.

Original language	English
Journal	Journal of Medical Radiation Sciences
Volume	66
Issue number	S1
DOIs	https://doi.org/10.1002/jmrs.324
Publication status	Published - 29 Mar 2019
Event	ASMIRT / AACRT 2019 International Conference - Adelaide, Australia Duration: 28 Mar 2019 → 31 Mar 2019

UN SDGs

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

SDG 3 Good Health and Well-being

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10.1002/jmrs.324

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Camps, S. M., Houben, T., Edwards, C., Antico, M., Dunnhofer, M., Martens, E., Baeza, J., Vanneste, B. G. L., Van Limbergen, E., de With, P. H. N., Verhaegen, F. J. W., Carneiro, G., & Fontanarosa, D. (2019). Automatic deep learning based quality assessment of transperineal ultrasound guided prostate radiotherapy. Journal of Medical Radiation Sciences, 66(S1). https://doi.org/10.1002/jmrs.324

@article{be0be608ed84476f97394ce65619fe9d,

title = "Automatic deep learning based quality assessment of transperineal ultrasound guided prostate radiotherapy",

abstract = "Ultrasound (US) is one of the imaging modalities that can be used for image‐guided radiotherapy (RT) workflows of prostate cancer patients. It allows real‐time volumetric tracking during the course of the RT treatment, which could potentially improve the precision of radiation dose delivery. However, intra‐fraction motion management using US image guidance is not yet widespread. This can be partially attributed to the need for image interpretation by a trained operator during or after US image acquisition.",

author = "Camps, \{Saskia M.\} and Tim Houben and Christopher Edwards and Maria Antico and Matteo Dunnhofer and Esther Martens and Jose Baeza and Vanneste, \{Ben G.L.\} and \{Van Limbergen\}, Evert and \{de With\}, \{Peter H.N.\} and Verhaegen, \{Frank J.W.\} and Gustavo Carneiro and D. Fontanarosa",

year = "2019",

month = mar,

day = "29",

doi = "10.1002/jmrs.324",

language = "English",

volume = "66",

journal = "Journal of Medical Radiation Sciences",

issn = "2051-3895",

publisher = "Wiley",

number = "S1",

note = "ASMIRT / AACRT 2019 International Conference, ASMIRT 2019/AACRT 2019 ; Conference date: 28-03-2019 Through 31-03-2019",

}

Camps, SM, Houben, T, Edwards, C, Antico, M, Dunnhofer, M, Martens, E, Baeza, J, Vanneste, BGL, Van Limbergen, E, de With, PHN, Verhaegen, FJW, Carneiro, G & Fontanarosa, D 2019, 'Automatic deep learning based quality assessment of transperineal ultrasound guided prostate radiotherapy', Journal of Medical Radiation Sciences, vol. 66, no. S1. https://doi.org/10.1002/jmrs.324

TY - JOUR

T1 - Automatic deep learning based quality assessment of transperineal ultrasound guided prostate radiotherapy

AU - Camps, Saskia M.

AU - Houben, Tim

AU - Edwards, Christopher

AU - Antico, Maria

AU - Dunnhofer, Matteo

AU - Martens, Esther

AU - Baeza, Jose

AU - Vanneste, Ben G.L.

AU - Van Limbergen, Evert

AU - de With, Peter H.N.

AU - Verhaegen, Frank J.W.

AU - Carneiro, Gustavo

AU - Fontanarosa, D.

PY - 2019/3/29

Y1 - 2019/3/29

N2 - Ultrasound (US) is one of the imaging modalities that can be used for image‐guided radiotherapy (RT) workflows of prostate cancer patients. It allows real‐time volumetric tracking during the course of the RT treatment, which could potentially improve the precision of radiation dose delivery. However, intra‐fraction motion management using US image guidance is not yet widespread. This can be partially attributed to the need for image interpretation by a trained operator during or after US image acquisition.

AB - Ultrasound (US) is one of the imaging modalities that can be used for image‐guided radiotherapy (RT) workflows of prostate cancer patients. It allows real‐time volumetric tracking during the course of the RT treatment, which could potentially improve the precision of radiation dose delivery. However, intra‐fraction motion management using US image guidance is not yet widespread. This can be partially attributed to the need for image interpretation by a trained operator during or after US image acquisition.

U2 - 10.1002/jmrs.324

DO - 10.1002/jmrs.324

M3 - Conference article

SN - 2051-3895

VL - 66

JO - Journal of Medical Radiation Sciences

JF - Journal of Medical Radiation Sciences

IS - S1

T2 - ASMIRT / AACRT 2019 International Conference

Y2 - 28 March 2019 through 31 March 2019

ER -

Automatic deep learning based quality assessment of transperineal ultrasound guided prostate radiotherapy

Abstract

UN SDGs

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