# Robust Image Registration with Absent Correspondences in Pre-operative and Follow-up Brain MRI Scans of Diffuse Glioma Patients

arXiv: https://arxiv.org/abs/2210.11045 · HTML: https://mlsanity.com/paper/2210.11045/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 21 references that are in the map.
- First posted on arXiv 20 Oct 2022.

## Builds on (most cited first, 13 of 13)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Unsupervised Learning Model for Deformable Medical Image Registration](https://mlsanity.com/paper/1802.02604/) (2018)
- [Large Deformation Diffeomorphic Image Registration with Laplacian Pyramid Networks](https://mlsanity.com/paper/2006.16148/) (2020)
- [Unsupervised Learning for Fast Probabilistic Diffeomorphic Registration](https://mlsanity.com/paper/1805.04605/) (2018)
- [Fast Symmetric Diffeomorphic Image Registration with Convolutional Neural Networks](https://mlsanity.com/paper/2003.09514/) (2020)
- [End-to-End Unsupervised Deformable Image Registration with a Convolutional Neural Network](https://mlsanity.com/paper/1704.06065/) (2017)
- [Conditional Deformable Image Registration with Convolutional Neural Network](https://mlsanity.com/paper/2106.12673/) (2021)
- [Dual-Stream Pyramid Registration Network](https://mlsanity.com/paper/1909.11966/) (2019)
- [Closing the Gap between Deep and Conventional Image Registration using Probabilistic Dense Displacement Networks](https://mlsanity.com/paper/1907.10931/) (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)
- [Unsupervised Deformable Image Registration Using Cycle-Consistent CNN](https://mlsanity.com/paper/1907.01319/) (2019)
- [Unsupervised Deformable Image Registration with Absent Correspondences in Pre-operative and Post-Recurrence Brain Tumor MRI Scans](https://mlsanity.com/paper/2206.03900/) (2022)
- [A Deep Network for Joint Registration and Reconstruction of Images with Pathologies](https://mlsanity.com/paper/2008.07628/) (2020)

## Built on by (most cited first, 9 of 9)

- [A survey on deep learning in medical image registration: new technologies, uncertainty, evaluation metrics, and beyond](https://mlsanity.com/paper/2307.15615/) (2023)
- [Modality-Agnostic Structural Image Representation Learning for Deformable Multi-Modality Medical Image Registration](https://mlsanity.com/paper/2402.18933/) (2024)
- [Brain Tumor Sequence Registration with Non-iterative Coarse-to-fine Networks and Dual Deep Supervision](https://mlsanity.com/paper/2211.07876/) (2022)
- [Towards Saner Deep Image Registration](https://mlsanity.com/paper/2307.09696/) (2023)
- [Medical Image Registration and Its Application in Retinal Images: A Review](https://mlsanity.com/paper/2403.16502/) (2024)
- [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)
- [Efficient Large-Deformation Medical Image Registration via Recurrent Dynamic Correlation](https://mlsanity.com/paper/2510.22380/) (2025)
- [Deep-Learning Atlas Registration for Melanoma Brain Metastases: Preserving Pathology While Enabling Cohort-Level Analyses](https://mlsanity.com/paper/2602.12933/) (2026)

## Abstract (arXiv)

Registration of pre-operative and follow-up brain MRI scans is challenging due to the large variation of tissue appearance and missing correspondences in tumour recurrence regions caused by tumour mass effect. Although recent deep learning-based deformable registration methods have achieved remarkable success in various medical applications, most of them are not capable of registering images with pathologies. In this paper, we propose a 3-step registration pipeline for pre-operative and follow-up brain MRI scans that consists of 1) a multi-level affine registration, 2) a conditional deep Laplacian pyramid image registration network (cLapIRN) with forward-backward consistency constraint, and 3) a non-linear instance optimization method. We apply the method to the Brain Tumor Sequence Registration (BraTS-Reg) Challenge. Our method achieves accurate and robust registration of brain MRI scans with pathologies, which achieves a median absolute error of 1.64 mm and 88% of successful registration rate in the validation set of BraTS-Reg challenge. Our method ranks 1st place in the 2022 MICCAI BraTS-Reg challenge.