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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)
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.
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Publikationstyp
Artikel: Konferenzbeitrag
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,
Seiten: 1-10
Verlag
Springer
Verlagsort
Berlin [u.a.]
Nichtpatentliteratur
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