# The Brain Tumor Sequence Registration (BraTS-Reg) Challenge: Establishing Correspondence Between Pre-Operative and Follow-up MRI Scans of Diffuse Glioma Patients

arXiv: https://arxiv.org/abs/2112.06979 · HTML: https://mlsanity.com/paper/2112.06979/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 87 references that are in the map.
- First posted on arXiv 13 Dec 2021.

## Builds on (most cited first, 16 of 16)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [VoxelMorph: A Learning Framework for Deformable Medical Image Registration](https://mlsanity.com/paper/1809.05231/) (2018)
- [The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification](https://mlsanity.com/paper/2107.02314/) (2021)
- [Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge](https://mlsanity.com/paper/1811.02629/) (2018)
- [SynthMorph: learning contrast-invariant registration without acquired images](https://mlsanity.com/paper/2004.10282/) (2020)
- [Automated brain extraction of multi-sequence MRI using artificial neural networks](https://mlsanity.com/paper/1901.11341/) (2019)
- [Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning](https://mlsanity.com/paper/2112.04489/) (2021)
- [Unsupervised Learning for Fast Probabilistic Diffeomorphic Registration](https://mlsanity.com/paper/1805.04605/) (2018)
- [HyperMorph: Amortized Hyperparameter Learning for Image Registration](https://mlsanity.com/paper/2101.01035/) (2021)
- [Conditional Deformable Image Registration with Convolutional Neural Network](https://mlsanity.com/paper/2106.12673/) (2021)
- [Estimation of Large Motion in Lung CT by Integrating Regularized Keypoint Correspondences into Dense Deformable Registration](https://mlsanity.com/paper/1807.00467/) (2018)
- [Learn-Morph-Infer: a new way of solving the inverse problem for brain tumor modeling](https://mlsanity.com/paper/2111.04090/) (2021)
- [Evaluation of MRI to ultrasound registration methods for brain shift correction: The CuRIOUS2018 Challenge](https://mlsanity.com/paper/1904.10535/) (2019)
- [A Deep Network for Joint Registration and Reconstruction of Images with Pathologies](https://mlsanity.com/paper/2008.07628/) (2020)
- [Linear and Deformable Image Registration with 3D Convolutional Neural Networks](https://mlsanity.com/paper/1809.06226/) (2018)
- [Uncertainty-Aware Annotation Protocol to Evaluate Deformable Registration Algorithms](https://mlsanity.com/paper/2104.01217/) (2021)

## Built on by (most cited first, 30 of 32)

- [Correlation-aware Coarse-to-fine MLPs for Deformable Medical Image Registration](https://mlsanity.com/paper/2406.00123/) (2024)
- [Non-iterative Coarse-to-fine Transformer Networks for Joint Affine and Deformable Image Registration](https://mlsanity.com/paper/2307.03421/) (2023)
- [multiGradICON: A Foundation Model for Multimodal Medical Image Registration](https://mlsanity.com/paper/2408.00221/) (2024)
- [Unsupervised Deformable Image Registration with Absent Correspondences in Pre-operative and Post-Recurrence Brain Tumor MRI Scans](https://mlsanity.com/paper/2206.03900/) (2022)
- [Brain Tumor Sequence Registration with Non-iterative Coarse-to-fine Networks and Dual Deep Supervision](https://mlsanity.com/paper/2211.07876/) (2022)
- [Robust Image Registration with Absent Correspondences in Pre-operative and Follow-up Brain MRI Scans of Diffuse Glioma Patients](https://mlsanity.com/paper/2210.11045/) (2022)
- [Unsupervised Method for Intra-patient Registration of Brain Magnetic Resonance Images based on Objective Function Weighting by Inverse Consistency: Contribution to the BraTS-Reg Challenge](https://mlsanity.com/paper/2211.07386/) (2022)
- [3D Inception-Based TransMorph: Pre- and Post-operative Multi-contrast MRI Registration in Brain Tumors](https://mlsanity.com/paper/2212.04579/) (2022)
- [Disentangling Progress in Medical Image Registration: Beyond Trend-Driven Architectures towards Domain-Specific Strategies](https://mlsanity.com/paper/2512.01913/) (2025)
- [Integrative Imaging Informatics for Cancer Research: Workflow Automation for Neuro-oncology (I3CR-WANO)](https://mlsanity.com/paper/2210.03151/) (2022)
- [Towards Saner Deep Image Registration](https://mlsanity.com/paper/2307.09696/) (2023)
- [Preserving Tumor Volumes for Unsupervised Medical Image Registration](https://mlsanity.com/paper/2309.10153/) (2023)
- [PULPo: Probabilistic Unsupervised Laplacian Pyramid Registration](https://mlsanity.com/paper/2407.10567/) (2024)
- [TimeFlow: Temporal Conditioning for Longitudinal Brain MRI Registration and Aging Analysis](https://mlsanity.com/paper/2501.08667/) (2025)
- [WSSAMNet: Weakly Supervised Semantic Attentive Medical Image Registration Network](https://mlsanity.com/paper/2203.07114/) (2022)
- [Self-supervised iRegNet for the Registration of Longitudinal Brain MRI of Diffuse Glioma Patients](https://mlsanity.com/paper/2211.11025/) (2022)
- [Co-Learning Semantic-aware Unsupervised Segmentation for Pathological Image Registration](https://mlsanity.com/paper/2310.11040/) (2023)
- [PREDICT-GBM: A multi-center platform to advance personalized glioblastoma radiotherapy planning](https://mlsanity.com/paper/2509.13360/) (2025)
- [Primitive Simultaneous Optimization of Similarity Metrics for Image Registration](https://mlsanity.com/paper/2304.01601/) (2023)
- [A Lightweight Optimization Framework for Estimating 3D Brain Tumor Infiltration](https://mlsanity.com/paper/2412.13811/) (2024)
- [Advancing Deformable Medical Image Registration with Multi-axis Cross-covariance Attention](https://mlsanity.com/paper/2412.18545/) (2024)
- [Uncertainty Estimation for Pretrained Medical Image Registration Models via Transformation Equivariance](https://mlsanity.com/paper/2509.23355/) (2025)
- [Framing image registration as a landmark detection problem for label-noise-aware task representation (HitR)](https://mlsanity.com/paper/2308.01318/) (2023)
- [USLR: an open-source tool for unbiased and smooth longitudinal registration of brain MR](https://mlsanity.com/paper/2311.08371/) (2023)
- [Utilizing Radiomic Feature Analysis For Automated MRI Keypoint Detection: Enhancing Graph Applications](https://mlsanity.com/paper/2311.18281/) (2023)
- [OncoReg: Medical Image Registration for Oncological Challenges](https://mlsanity.com/paper/2503.23179/) (2025)
- [Toward Next-generation Medical Vision Backbones: Modeling Finer-grained Long-range Visual Dependency](https://mlsanity.com/paper/2509.11328/) (2025)
- [TraceTrans: Translation and Spatial Tracing for Surgical Prediction](https://mlsanity.com/paper/2510.22379/) (2025)
- [BrainNormalizer: Anatomy-Informed Pseudo-Healthy Brain Reconstruction from Tumor MRI via Edge-Guided ControlNet](https://mlsanity.com/paper/2511.12853/) (2025)
- [Data-driven registration and modeling of brain deformation for image-guided neurosurgery](https://mlsanity.com/paper/2602.10155/) (2026)

## Abstract (arXiv)

Registration of longitudinal brain MRI scans containing pathologies is challenging due to dramatic changes in tissue appearance. Although there has been progress in developing general-purpose medical image registration techniques, they have not yet attained the requisite precision and reliability for this task, highlighting its inherent complexity. Here we describe the Brain Tumor Sequence Registration (BraTS-Reg) challenge, as the first public benchmark environment for deformable registration algorithms focusing on estimating correspondences between pre-operative and follow-up scans of the same patient diagnosed with a diffuse brain glioma. The BraTS-Reg data comprise de-identified multi-institutional multi-parametric MRI (mpMRI) scans, curated for size and resolution according to a canonical anatomical template, and divided into training, validation, and testing sets. Clinical experts annotated ground truth (GT) landmark points of anatomical locations distinct across the temporal domain. Quantitative evaluation and ranking were based on the Median Euclidean Error (MEE), Robustness, and the determinant of the Jacobian of the displacement field. The top-ranked methodologies yielded similar performance across all evaluation metrics and shared several methodological commonalities, including pre-alignment, deep neural networks, inverse consistency analysis, and test-time instance optimization per-case basis as a post-processing step. The top-ranked method attained the MEE at or below that of the inter-rater variability for approximately 60% of the evaluated landmarks, underscoring the scope for further accuracy and robustness improvements, especially relative to human experts. The aim of BraTS-Reg is to continue to serve as an active resource for research, with the data and online evaluation tools accessible at https://bratsreg.github.io/.