Large Scale Unsupervised Brain MRI Image Registration Solution for Learn2Reg 2024
This work addresses the problem of anatomical structure alignment in brain MRI images for medical imaging researchers, representing an incremental improvement over existing methods.
The paper tackled unsupervised brain MRI image registration between different patients without segmentation labels on a large dataset, achieving a Dice coefficient of 77.34% which was 1.4% higher than TransMorph and securing second place in the Learn2Reg 2024 challenge.
In this paper, we summarize the methods and experimental results we proposed for Task 2 in the learn2reg 2024 Challenge. This task focuses on unsupervised registration of anatomical structures in brain MRI images between different patients. The difficulty lies in: (1) without segmentation labels, and (2) a large amount of data. To address these challenges, we built an efficient backbone network and explored several schemes to further enhance registration accuracy. Under the guidance of the NCC loss function and smoothness regularization loss function, we obtained a smooth and reasonable deformation field. According to the leaderboard, our method achieved a Dice coefficient of 77.34%, which is 1.4% higher than the TransMorph. Overall, we won second place on the leaderboard for Task 2.