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.
|Number of pages||2|
|Journal||European Urology Supplements|
|Publication status||Published - 2019|
|Event||34th Annual EAU Congress European Association of Urology (EAU19) - Fira Gran Via, Barcelona, Spain|
Duration: 15 Mar 2019 → 19 Mar 2019
Bibliographical noteAbstracts EAU19 – 34th Annual EAU Congress
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