# SegReg: Segmenting OARs by Registering MR Images and CT Annotations

arXiv: https://arxiv.org/abs/2311.06956 · HTML: https://mlsanity.com/paper/2311.06956/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 37 references that are in the map.
- First posted on arXiv 12 Nov 2023.

## Builds on (most cited first, 5 of 5)

- [BIAS: Transparent reporting of biomedical image analysis challenges](https://mlsanity.com/paper/1910.04071/) (2019)
- [Modality-aware Mutual Learning for Multi-modal Medical Image Segmentation](https://mlsanity.com/paper/2107.09842/) (2021)
- [Organ at Risk Segmentation for Head and Neck Cancer using Stratified Learning and Neural Architecture Search](https://mlsanity.com/paper/2004.08426/) (2020)
- [Automatic Segmentation of Organs-at-Risk from Head-and-Neck CT using Separable Convolutional Neural Network with Hard-Region-Weighted Loss](https://mlsanity.com/paper/2102.01897/) (2021)
- [Progressively refined deep joint registration segmentation (ProRSeg) of gastrointestinal organs at risk: Application to MRI and cone-beam CT](https://mlsanity.com/paper/2210.14297/) (2022)

## Built on by (most cited first, 16 of 16)

- [MedConv: Convolutions Beat Transformers on Long-Tailed Bone Density Prediction](https://mlsanity.com/paper/2502.00631/) (2025)
- [ESA: Annotation-Efficient Active Learning for Semantic Segmentation](https://mlsanity.com/paper/2408.13491/) (2024)
- [MSDet: Receptive Field Enhanced Multiscale Detection for Tiny Pulmonary Nodule](https://mlsanity.com/paper/2409.14028/) (2024)
- [SegStitch: Multidimensional Transformer for Robust and Efficient Medical Imaging Segmentation](https://mlsanity.com/paper/2408.00496/) (2024)
- [MedDet: Generative Adversarial Distillation for Efficient Cervical Disc Herniation Detection](https://mlsanity.com/paper/2409.00204/) (2024)
- [GAMED-Snake: Gradient-aware Adaptive Momentum Evolution Deep Snake Model for Multi-organ Segmentation](https://mlsanity.com/paper/2501.12844/) (2025)
- [PathoHR: Breast Cancer Survival Prediction on High-Resolution Pathological Images](https://mlsanity.com/paper/2503.17970/) (2025)
- [Medical Artificial Intelligence for Early Detection of Lung Cancer: A Survey](https://mlsanity.com/paper/2410.14769/) (2024)
- [MMCLIP: Cross-modal Attention Masked Modelling for Medical Language-Image Pre-Training](https://mlsanity.com/paper/2407.19546/) (2024)
- [DOEI: Dual Optimization of Embedding Information for Attention-Enhanced Class Activation Maps](https://mlsanity.com/paper/2502.15885/) (2025)
- [SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentation](https://mlsanity.com/paper/2506.08949/) (2025)
- [A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation](https://mlsanity.com/paper/2502.05396/) (2025)
- [3D Medical Imaging Segmentation on Non-Contrast CT](https://mlsanity.com/paper/2503.08361/) (2025)
- [A Large Convolutional Neural Network for Clinical Target and Multi-organ Segmentation in Gynecologic Brachytherapy with Multi-stage Learning](https://mlsanity.com/paper/2506.01073/) (2025)
- [Query Nearby: Offset-Adjusted Mask2Former enhances small-organ segmentation](https://mlsanity.com/paper/2506.05897/) (2025)
- [SegTTA: Training-Free Test-Time Augmentation for Zero-Shot Medical Imaging Segmentation](https://mlsanity.com/paper/2604.17451/) (2026)

## Abstract (arXiv)

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