SegReg: Segmenting OARs by Registering MR Images and CT Annotations
This work addresses the time-consuming and costly manual segmentation process in clinical radiotherapy, enabling more efficient treatment planning for patients with head and neck tumors.
The paper tackles the problem of automating organ-at-risk segmentation for radiotherapy planning by registering MRI to CT scans, achieving a 16.78% improvement in mDSC and 18.77% in mIoU over CT-only baselines.
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. See project website https://steve-zeyu-zhang.github.io/SegReg