# 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

arXiv: https://arxiv.org/abs/2211.07386 · HTML: https://mlsanity.com/paper/2211.07386/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 12 references that are in the map.
- First posted on arXiv 14 Nov 2022.

## Builds on (most cited first, 8 of 8)

- [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)
- [SynthMorph: learning contrast-invariant registration without acquired images](https://mlsanity.com/paper/2004.10282/) (2020)
- [Large Deformation Diffeomorphic Image Registration with Laplacian Pyramid Networks](https://mlsanity.com/paper/2006.16148/) (2020)
- [Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning](https://mlsanity.com/paper/2112.04489/) (2021)
- [Fast Symmetric Diffeomorphic Image Registration with Convolutional Neural Networks](https://mlsanity.com/paper/2003.09514/) (2020)
- [A Framework For Contrastive Self-Supervised Learning And Designing A New Approach](https://mlsanity.com/paper/2009.00104/) (2020)
- [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)

## Built on by (most cited first, 5 of 5)

- [Preserving Tumor Volumes for Unsupervised Medical Image Registration](https://mlsanity.com/paper/2309.10153/) (2023)
- [Primitive Simultaneous Optimization of Similarity Metrics for Image Registration](https://mlsanity.com/paper/2304.01601/) (2023)
- [USLR: an open-source tool for unbiased and smooth longitudinal registration of brain MR](https://mlsanity.com/paper/2311.08371/) (2023)
- [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)

Registration of brain scans with pathologies is difficult, yet important research area. The importance of this task motivated researchers to organize the BraTS-Reg challenge, jointly with IEEE ISBI 2022 and MICCAI 2022 conferences. The organizers introduced the task of aligning pre-operative to follow-up magnetic resonance images of glioma. The main difficulties are connected with the missing data leading to large, nonrigid, and noninvertible deformations. In this work, we describe our contributions to both the editions of the BraTS-Reg challenge. The proposed method is based on combined deep learning and instance optimization approaches. First, the instance optimization enriches the state-of-the-art LapIRN method to improve the generalizability and fine-details preservation. Second, an additional objective function weighting is introduced, based on the inverse consistency. The proposed method is fully unsupervised and exhibits high registration quality and robustness. The quantitative results on the external validation set are: (i) IEEE ISBI 2022 edition: 1.85, and 0.86, (ii) MICCAI 2022 edition: 1.71, and 0.86, in terms of the mean of median absolute error and robustness respectively. The method scored the 1st place during the IEEE ISBI 2022 version of the challenge and the 3rd place during the MICCAI 2022. Future work could transfer the inverse consistency-based weighting directly into the deep network training.