SegReg: Segmenting OARs by Registering MR Images and CT Annotations

Zeyu Zhang, Xuyin Qi, Bowen Zhang, Biao Wu, Hien Le +6 more
11/12/2023
cs.CV

No official code found. Best implementation: steve-zeyu-zhang/SegReg (0 stars, last commit 2024-08-30).

Abstract

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

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Code Implementations(1)

steve-zeyu-zhang/SegRegCommunity100%

[ISBI 2024] SegReg: Segmenting OARs by Registering MR Images and CT Annotations

00HTML, PythonNov 13, 20232 years ago
computer-visiondeep-learningimage-registrationmedical-image-analysismedical-image-processing+4 more

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Cite this paper

@article{zhang2023segreg,
  title  = {SegReg: Segmenting OARs by Registering MR Images and CT Annotations},
  author = {Zeyu Zhang and Xuyin Qi and Bowen Zhang and Biao Wu and Hien Le and Bora Jeong and Zhibin Liao and Yunxiang Liu and Johan Verjans and Minh-Son To and Richard Hartley},
  year   = {2023},
  eprint = {2311.06956},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2311.06956v3}
}

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