PuSH - Publikationsserver des Helmholtz Zentrums München

De Benetti, F.* ; Yaganeh, Y.* ; Belka, C.* ; Corradini, S.* ; Navab, N.* ; Kurz, C.* ; Landry, G.* ; Albarqouni, S. ; Wendler, T.*

CloverNet – Leveraging planning annotations for enhanced procedural MR segmentation: An application to adaptive radiation therapy.

In: (Clinical Image-Based Procedures). Berlin [u.a.]: Springer, 2024. 1-10 (Lect. Notes Comput. Sc. ; 15196 LNCS)
DOI
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
In radiation therapy (RT), an accurate delineation of the regions of interest (ROI) and organs at risk (OAR) allows for a more targeted irradiation with reduced side effects. The current clinical workflow for combined MR-linear accelerator devices (MR-linacs) requires the acquisition of a planning MR volume (MR-P), in which the ROI and OAR are accurately segmented by the clinical team. These segmentation maps (S-P) are transferred to the MR acquired on the day of the RT fraction (MR-Fx) using registration, followed by time-consuming manual corrections. The goal of this paper is to enable accurate automatic segmentation of MR-Fx using S-P without clinical workflow disruption. We propose a novel UNet-based architecture, CloverNet, that takes as inputs MR-Fx and S-P in two separate encoder branches, whose latent spaces are concatenated in the bottleneck to generate an improved segmentation of MP-Fx. CloverNet improves the absolute Dice Score by 3.73% (relative +4.34%, p<0.001) when compared with conventional 3D UNet. Moreover, we believe this approach is potentially applicable to other longitudinal use cases in which a prior segmentation of the ROI is available.
Altmetric
Weitere Metriken?
[➜Einloggen]
Zusatzinfos bearbeiten [➜Einloggen]
Publikationstyp Artikel: Konferenzbeitrag
Korrespondenzautor
Schlagwörter Mr-linac ; Mri ; Patient-specific Segmentation ; Radiation Therapy
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Konferenztitel Clinical Image-Based Procedures
Zeitschrift Lecture Notes in Computer Science
Quellenangaben Band: 15196 LNCS, Heft: , Seiten: 1-10 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Nichtpatentliteratur Publikationen
Institut(e) Helmholtz Artifical Intelligence Cooperation Unit (HAICU)