SegReg: Segmenting OARs by Registering MR Images and CT Annotations
arXiv (Cornell University)(2023)
摘要
Organ at risk (OAR) segmentation is a critical process in radiotherapy
treatment planning such as head and neck tumors. Nevertheless, in clinical
practice, radiation oncologists predominantly perform OAR segmentations
manually on CT scans. This manual process is highly time-consuming and
expensive, limiting the number of patients who can receive timely radiotherapy.
Additionally, CT scans offer lower soft-tissue contrast compared to MRI.
Despite MRI providing superior soft-tissue visualization, its time-consuming
nature makes it infeasible for real-time treatment planning. To address these
challenges, we propose a method called SegReg, which utilizes Elastic Symmetric
Normalization for registering MRI to perform OAR segmentation. SegReg
outperforms the CT-only baseline by 16.78
that it effectively combines the geometric accuracy of CT with the superior
soft-tissue contrast of MRI, making accurate automated OAR segmentation for
clinical practice become possible. See project website
https://steve-zeyu-zhang.github.io/SegReg
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