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CloverNet--Leveraging Planning Annotations for Enhanced Procedural MR Segmentation: An Application to Adaptive Radiation Therapy
Clinical Image-Based Procedures — CLIP 2024 workshop @ MICCAI, Lecture Notes in Computer Science, 2024
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 …
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Cite (BibTeX)
@inproceedings{de2024clovernet,
author = {De Benetti, Francesca and Yaganeh, Yousef and Belka, Claus and Corradini, Stefanie and Navab, Nassir and Kurz, Christopher and Landry, Guillaume and Albarqouni, Shadi and Wendler, Thomas},
booktitle = {Workshop on Clinical Image-Based Procedures},
organization = {Springer Nature Switzerland Cham},
pages = {1--10},
title = {CloverNet--Leveraging Planning Annotations for Enhanced Procedural MR Segmentation: An Application to Adaptive Radiation Therapy},
year = {2024}
}