mlsanityData to 14 Oct 2026

SegReg: Segmenting OARs by Registering MR Images and CT Annotations

arXiv 2311.06956 ↗First posted 12 Nov 2023cs.CVcited by 16

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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

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

5 of 37 references.

References · 5

  1. 2019BIAS: Transparent reporting of biomedical image analysis challengescited by 57
  2. 2021Modality-aware Mutual Learning for Multi-modal Medical Image Segmentationcited by 47
  3. 2020Organ at Risk Segmentation for Head and Neck Cancer using Stratified Learning and Neural Architecture Searchcited by 23
  4. 2021Automatic Segmentation of Organs-at-Risk from Head-and-Neck CT using Separable Convolutional Neural Network with Hard-Region-Weighted Losscited by 9
  5. 2022Progressively refined deep joint registration segmentation (ProRSeg) of gastrointestinal organs at risk: Application to MRI and cone-beam CTcited by 5

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 6; 2025: 9; 2026: 1.

16 papers.

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  1. 2025MedConv: Convolutions Beat Transformers on Long-Tailed Bone Density Predictioncited by 16
  2. 2024ESA: Annotation-Efficient Active Learning for Semantic Segmentationcited by 15
  3. 2024MSDet: Receptive Field Enhanced Multiscale Detection for Tiny Pulmonary Nodulecited by 14
  4. 2024SegStitch: Multidimensional Transformer for Robust and Efficient Medical Imaging Segmentationcited by 13
  5. 2024MedDet: Generative Adversarial Distillation for Efficient Cervical Disc Herniation Detectioncited by 11
  6. 2025GAMED-Snake: Gradient-aware Adaptive Momentum Evolution Deep Snake Model for Multi-organ Segmentationcited by 10
  7. 2025PathoHR: Breast Cancer Survival Prediction on High-Resolution Pathological Imagescited by 9
  8. 2024Medical Artificial Intelligence for Early Detection of Lung Cancer: A Surveycited by 5
  9. 2024MMCLIP: Cross-modal Attention Masked Modelling for Medical Language-Image Pre-Trainingcited by 4
  10. 2025DOEI: Dual Optimization of Embedding Information for Attention-Enhanced Class Activation Mapscited by 4
  11. 2025SSS: Semi-Supervised SAM-2 with Efficient Prompting for Medical Imaging Segmentationcited by 3
  12. 2025A Novel Convolutional-Free Method for 3D Medical Imaging Segmentationcited by 1
4 more
  1. 20253D Medical Imaging Segmentation on Non-Contrast CTcited by 0
  2. 2025A Large Convolutional Neural Network for Clinical Target and Multi-organ Segmentation in Gynecologic Brachytherapy with Multi-stage Learningcited by 0
  3. 2025Query Nearby: Offset-Adjusted Mask2Former enhances small-organ segmentationcited by 0
  4. 2026SegTTA: Training-Free Test-Time Augmentation for Zero-Shot Medical Imaging Segmentationcited by 0