SegReg: Segmenting OARs by Registering MR Images and CT Annotations
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866910349301645312 |
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| author | Zhang, Zeyu Qi, Xuyin Zhang, Bowen Wu, Biao Le, Hien Jeong, Bora Liao, Zhibin Liu, Yunxiang Verjans, Johan To, Minh-Son Hartley, Richard |
| author_facet | Zhang, Zeyu Qi, Xuyin Zhang, Bowen Wu, Biao Le, Hien Jeong, Bora Liao, Zhibin Liu, Yunxiang Verjans, Johan To, Minh-Son Hartley, Richard |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_06956 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | SegReg: Segmenting OARs by Registering MR Images and CT Annotations Zhang, Zeyu Qi, Xuyin Zhang, Bowen Wu, Biao Le, Hien Jeong, Bora Liao, Zhibin Liu, Yunxiang Verjans, Johan To, Minh-Son Hartley, Richard Computer Vision and Pattern Recognition 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 |
| title | SegReg: Segmenting OARs by Registering MR Images and CT Annotations |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2311.06956 |