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

Research output: Contribution to journalConference articleAcademicpeer-review

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.

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Prostate
Magnetic Resonance Imaging
Learning
Biopsy

Bibliographical note

Abstracts EAU19 – 34th Annual EAU Congress

Cite this

@article{7280a51392404cc39753d7852c09029e,
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",
year = "2019",
doi = "10.1016/S1569-9056(19)31363-6",
language = "English",
volume = "18",
pages = "e1880--e1881",
journal = "European Urology Supplements",
issn = "1569-9056",
publisher = "Elsevier",
number = "1",

}

PT159 - Automatic segmentation of the prostate in transrectal ultrasound images using deep learning for application in MRI-TRUS fusion. / van Sloun, R.J.G.; Wildeboer, R.R.; Postema, A.W.; Gayet, M.; Mannaerts, C.K.; Beerlage, H.P.; Salomon, Georg; Wijkstra, H.; Mischi, M.

In: European Urology Supplements, Vol. 18, No. 1, 2019, p. e1880-e1881.

Research output: Contribution to journalConference articleAcademicpeer-review

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

VL - 18

SP - e1880-e1881

JO - European Urology Supplements

T2 - European Urology Supplements

JF - European Urology Supplements

SN - 1569-9056

IS - 1

ER -