TY - GEN
T1 - CloverNet – Leveraging Planning Annotations for Enhanced Procedural MR Segmentation
T2 - 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
AU - De Benetti, Francesca
AU - Yaganeh, Yousef
AU - Belka, Claus
AU - Corradini, Stefanie
AU - Navab, Nassir
AU - Kurz, Christopher
AU - Landry, Guillaume
AU - Albarqouni, Shadi
AU - Wendler, Thomas
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - MR-linac
KW - MRI
KW - Patient-specific Segmentation
KW - Radiation Therapy
UR - http://www.scopus.com/inward/record.url?scp=85206134141&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-73083-2_1
DO - 10.1007/978-3-031-73083-2_1
M3 - Conference contribution
AN - SCOPUS:85206134141
SN - 9783031730825
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 1
EP - 10
BT - Clinical Image-Based Procedures - 13th International Workshop, CLIP 2024, Held in Conjunction with MICCAI 2024, Proceedings
A2 - Drechsler, Klaus
A2 - Oyarzun Laura, Cristina
A2 - Wesarg, Stefan
A2 - Freiman, Moti
A2 - Chen, Yufei
A2 - Erdt, Marius
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 6 October 2024 through 6 October 2024
ER -