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% in mDSC and 18.77% in mIoU, showing
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.

SegReg 方法利用弹性对称标准化技术将 MRI 图像与 CT 图像进行配准，以实现器官处于危险区域的分割，通过结合 CT 的几何精确度和 MRI 的软组织对比度，使得精确自动的器官处于危险区域的分割成为可能。