CloverNet – Leveraging Planning Annotations for Enhanced Procedural MR Segmentation: An Application to Adaptive Radiation Therapy
De Benetti F, Yaganeh Y, Belka C, Corradini S, Navab N, Kurz C, Landry G, Albarqouni S, Wendler T (2024)
Publication Type: Conference contribution
Publication year: 2024
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 15196 LNCS
Pages Range: 1-10
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Marrakesh, MAR
ISBN: 9783031730825
DOI: 10.1007/978-3-031-73083-2_1
Abstract
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.
Involved external institutions
Technische Universität München (TUM)
Germany (DE)
Klinikum der Universität München (LMU Klinikum)
Germany (DE)
Universitätsklinikum Bonn
Germany (DE)
Germany (DE)
Klinikum der Universität München (LMU Klinikum)
Germany (DE)
Universitätsklinikum Bonn
Germany (DE)
How to cite
APA:
De Benetti, F., Yaganeh, Y., Belka, C., Corradini, S., Navab, N., Kurz, C.,... Wendler, T. (2024). CloverNet – Leveraging Planning Annotations for Enhanced Procedural MR Segmentation: An Application to Adaptive Radiation Therapy. In Klaus Drechsler, Cristina Oyarzun Laura, Stefan Wesarg, Moti Freiman, Yufei Chen, Marius Erdt (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 1-10). Marrakesh, MAR: Springer Science and Business Media Deutschland GmbH.
MLA:
De Benetti, Francesca, et al. "CloverNet – Leveraging Planning Annotations for Enhanced Procedural MR Segmentation: An Application to Adaptive Radiation Therapy." Proceedings of the 13th International Workshop on Clinical Image-based Procedures: Towards Holistic Patient Models for Personalized Healthcare, CLIP 2024 held in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024, Marrakesh, MAR Ed. Klaus Drechsler, Cristina Oyarzun Laura, Stefan Wesarg, Moti Freiman, Yufei Chen, Marius Erdt, Springer Science and Business Media Deutschland GmbH, 2024. 1-10.
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