PT159 - Automatic segmentation of the prostate in transrectal ultrasound images using deep learning for application in MRI-TRUS fusion

R.J.G. van Sloun; R.R. Wildeboer; A.W. Postema; M. Gayet; C.K. Mannaerts; H.P. Beerlage; Georg Salomon; H. Wijkstra; M. Mischi

doi:10.1016/S1569-9056(19)31363-6

PT159 - Automatic segmentation of the prostate in transrectal ultrasound images using deep learning for application in MRI-TRUS fusion

R.J.G. van Sloun
, R.R. Wildeboer
, A.W. Postema
, M. Gayet
, C.K. Mannaerts
, H.P. Beerlage
, Georg Salomon
, H. Wijkstra
, M. Mischi (Corresponding author)

Onderzoeksoutput: Bijdrage aan tijdschrift › Congresartikel › peer review

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Introduction & Objectives: In recent years, prostate biopsy increasingly involves targeting magnetic resonance imaging (MRI)-suspicious lesions after fusion with real-time transrectal ultrasound (TRUS). Such fusion currently requires (semi)manual prostate delineation, burdening clinicians with this lengthy procedure. A reliable automatic prostate segmentation on TRUS is still an unsolved challenge; therefore, here we propose a real-time prostate segmentation algorithm through deep-learning that readily translates between different scanners and user settings.

Originele taal-2	Engels
Pagina's (van-tot)	e1880-e1881
Aantal pagina's	2
Tijdschrift	European Urology Supplements
Volume	18
Nummer van het tijdschrift	1
DOI's	https://doi.org/10.1016/S1569-9056(19)31363-6
Status	Gepubliceerd - 2019
Evenement	34th Annual EAU Congress European Association of Urology (EAU19) - Fira Gran Via, Barcelona, Spanje Duur: 15 mrt. 2019 → 19 mrt. 2019 https://eaucongress.uroweb.org/

Bibliografische nota

Abstracts EAU19 – 34th Annual EAU Congress

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10.1016/S1569-9056(19)31363-6

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van Sloun, R. J. G., Wildeboer, R. R., Postema, A. W., Gayet, M., Mannaerts, C. K., Beerlage, H. P., Salomon, G., Wijkstra, H., & Mischi, M. (2019). PT159 - Automatic segmentation of the prostate in transrectal ultrasound images using deep learning for application in MRI-TRUS fusion. European Urology Supplements, 18(1), e1880-e1881. https://doi.org/10.1016/S1569-9056(19)31363-6

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title = "PT159 - Automatic segmentation of the prostate in transrectal ultrasound images using deep learning for application in MRI-TRUS fusion",

abstract = "Introduction \& Objectives: In recent years, prostate biopsy increasingly involves targeting magnetic resonance imaging (MRI)-suspicious lesions after fusion with real-time transrectal ultrasound (TRUS). Such fusion currently requires (semi)manual prostate delineation, burdening clinicians with this lengthy procedure. A reliable automatic prostate segmentation on TRUS is still an unsolved challenge; therefore, here we propose a real-time prostate segmentation algorithm through deep-learning that readily translates between different scanners and user settings.",

author = "\{van Sloun\}, R.J.G. and R.R. Wildeboer and A.W. Postema and M. Gayet and C.K. Mannaerts and H.P. Beerlage and Georg Salomon and H. Wijkstra and M. Mischi",

note = "Abstracts EAU19 – 34th Annual EAU Congress; 34th Annual EAU Congress European Association of Urology (EAU19) ; Conference date: 15-03-2019 Through 19-03-2019",

year = "2019",

doi = "10.1016/S1569-9056(19)31363-6",

language = "English",

volume = "18",

pages = "e1880--e1881",

journal = "European Urology Supplements",

issn = "1569-9056",

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number = "1",

url = "https://eaucongress.uroweb.org/",

}

van Sloun, RJG, Wildeboer, RR, Postema, AW, Gayet, M, Mannaerts, CK, Beerlage, HP, Salomon, G, Wijkstra, H & Mischi, M 2019, 'PT159 - Automatic segmentation of the prostate in transrectal ultrasound images using deep learning for application in MRI-TRUS fusion', European Urology Supplements, vol. 18, nr. 1, blz. e1880-e1881. https://doi.org/10.1016/S1569-9056(19)31363-6

TY - JOUR

T1 - PT159 - Automatic segmentation of the prostate in transrectal ultrasound images using deep learning for application in MRI-TRUS fusion

AU - van Sloun, R.J.G.

AU - Wildeboer, R.R.

AU - Postema, A.W.

AU - Gayet, M.

AU - Mannaerts, C.K.

AU - Beerlage, H.P.

AU - Salomon, Georg

AU - Wijkstra, H.

AU - Mischi, M.

N1 - Abstracts EAU19 – 34th Annual EAU Congress

PY - 2019

Y1 - 2019

N2 - Introduction & Objectives: In recent years, prostate biopsy increasingly involves targeting magnetic resonance imaging (MRI)-suspicious lesions after fusion with real-time transrectal ultrasound (TRUS). Such fusion currently requires (semi)manual prostate delineation, burdening clinicians with this lengthy procedure. A reliable automatic prostate segmentation on TRUS is still an unsolved challenge; therefore, here we propose a real-time prostate segmentation algorithm through deep-learning that readily translates between different scanners and user settings.

AB - Introduction & Objectives: In recent years, prostate biopsy increasingly involves targeting magnetic resonance imaging (MRI)-suspicious lesions after fusion with real-time transrectal ultrasound (TRUS). Such fusion currently requires (semi)manual prostate delineation, burdening clinicians with this lengthy procedure. A reliable automatic prostate segmentation on TRUS is still an unsolved challenge; therefore, here we propose a real-time prostate segmentation algorithm through deep-learning that readily translates between different scanners and user settings.

U2 - 10.1016/S1569-9056(19)31363-6

DO - 10.1016/S1569-9056(19)31363-6

M3 - Conference article

SN - 1569-9056

VL - 18

SP - e1880-e1881

JO - European Urology Supplements

JF - European Urology Supplements

IS - 1

T2 - 34th Annual EAU Congress European Association of Urology (EAU19)

Y2 - 15 March 2019 through 19 March 2019

ER -

PT159 - Automatic segmentation of the prostate in transrectal ultrasound images using deep learning for application in MRI-TRUS fusion

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