Large Scale Unsupervised Brain MRI Image Registration Solution for Learn2Reg 2024

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, an...

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Bibliographic Details
Published inarXiv.org
Main Authors Zhang, Yuxi, Chen, Xiang, Wang, Jiazheng, Liu, Min, Wang, Yaonan, Liu, Dongdong, Hu, Renjiu, Zhang, Hang
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 04.09.2024
Subjects
Brain
Image enhancement
Image registration
Image segmentation
Magnetic resonance imaging
Medical imaging
Regularization
Smoothness
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Summary: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.
ISSN:2331-8422