# Brain Tumor Sequence Registration with Non-iterative Coarse-to-fine Networks and Dual Deep Supervision

arXiv: https://arxiv.org/abs/2211.07876 · HTML: https://mlsanity.com/paper/2211.07876/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 20 references that are in the map.
- First posted on arXiv 15 Nov 2022.

## Builds on (most cited first, 14 of 14)

- [VoxelMorph: A Learning Framework for Deformable Medical Image Registration](https://mlsanity.com/paper/1809.05231/) (2018)
- [Unsupervised Learning of Probabilistic Diffeomorphic Registration for Images and Surfaces](https://mlsanity.com/paper/1903.03545/) (2019)
- [Large Deformation Diffeomorphic Image Registration with Laplacian Pyramid Networks](https://mlsanity.com/paper/2006.16148/) (2020)
- [Deep Learning in Medical Image Registration: A Survey](https://mlsanity.com/paper/1903.02026/) (2019)
- [Recursive Cascaded Networks for Unsupervised Medical Image Registration](https://mlsanity.com/paper/1907.12353/) (2019)
- [Dual-Stream Pyramid Registration Network](https://mlsanity.com/paper/1909.11966/) (2019)
- [Image-and-Spatial Transformer Networks for Structure-Guided Image Registration](https://mlsanity.com/paper/1907.09200/) (2019)
- [The Brain Tumor Sequence Registration (BraTS-Reg) Challenge: Establishing Correspondence Between Pre-Operative and Follow-up MRI Scans of Diffuse Glioma Patients](https://mlsanity.com/paper/2112.06979/) (2021)
- [Joint Progressive and Coarse-to-fine Registration of Brain MRI via Deformation Field Integration and Non-Rigid Feature Fusion](https://mlsanity.com/paper/2109.12384/) (2021)
- [Non-iterative Coarse-to-fine Registration based on Single-pass Deep Cumulative Learning](https://mlsanity.com/paper/2206.12596/) (2022)
- [FAIM -- A ConvNet Method for Unsupervised 3D Medical Image Registration](https://mlsanity.com/paper/1811.09243/) (2018)
- [Enhancing Medical Image Registration via Appearance Adjustment Networks](https://mlsanity.com/paper/2103.05213/) (2021)
- [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)
- [High-quality Panorama Stitching based on Asymmetric Bidirectional Optical Flow](https://mlsanity.com/paper/2006.01201/) (2020)

## Built on by (most cited first, 10 of 10)

- [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)
- [AutoFuse: Automatic Fusion Networks for Deformable Medical Image Registration](https://mlsanity.com/paper/2309.05271/) (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)
- [Primitive Simultaneous Optimization of Similarity Metrics for Image Registration](https://mlsanity.com/paper/2304.01601/) (2023)
- [Advancing Deformable Medical Image Registration with Multi-axis Cross-covariance Attention](https://mlsanity.com/paper/2412.18545/) (2024)
- [Toward Next-generation Medical Vision Backbones: Modeling Finer-grained Long-range Visual Dependency](https://mlsanity.com/paper/2509.11328/) (2025)
- [Data-driven registration and modeling of brain deformation for image-guided neurosurgery](https://mlsanity.com/paper/2602.10155/) (2026)
- [Deep-Learning Atlas Registration for Melanoma Brain Metastases: Preserving Pathology While Enabling Cohort-Level Analyses](https://mlsanity.com/paper/2602.12933/) (2026)

## Abstract (arXiv)

In this study, we focus on brain tumor sequence registration between pre-operative and follow-up Magnetic Resonance Imaging (MRI) scans of brain glioma patients, in the context of Brain Tumor Sequence Registration challenge (BraTS-Reg 2022). Brain tumor registration is a fundamental requirement in brain image analysis for quantifying tumor changes. This is a challenging task due to large deformations and missing correspondences between pre-operative and follow-up scans. For this task, we adopt our recently proposed Non-Iterative Coarse-to-finE registration Networks (NICE-Net) - a deep learning-based method for coarse-to-fine registering images with large deformations. To overcome missing correspondences, we extend the NICE-Net by introducing dual deep supervision, where a deep self-supervised loss based on image similarity and a deep weakly-supervised loss based on manually annotated landmarks are deeply embedded into the NICE-Net. At the BraTS-Reg 2022, our method achieved a competitive result on the validation set (mean absolute error: 3.387) and placed 4th in the final testing phase (Score: 0.3544).