无标注脑部MRI图像配准，实现高精度跨患者结构对齐
Large Scale Unsupervised Brain MRI Image Registration Solution for Learn2Reg 2024
- 基于NCC与平滑正则化损失，构建高效配准网络
- 在无标签条件下取得77.34%的Dice系数，领先TransMorph 1.4%
- 适用于大规模医学图像配准，适合医疗影像研究者
本文总结了我们在Learn2Reg 2024挑战赛任务2中提出的算法与实验结果。该任务聚焦于不同患者间脑部MRI图像解剖结构的无监督配准。难点在于：(1) 缺乏分割标签，(2) 数据量庞大。为此，我们设计了一种高效的主干网络，并探索多种方案以进一步提升配准精度。在NCC损失函数与平滑性正则化损失函数的共同引导下，获得了平滑合理的形变场。根据排行榜数据，我们的方法在测试集上取得了77.34%的Dice系数，较TransMorph高出1.4%。总体而言，我们在任务2中获得第二名。
原文摘要 · Abstract (English)
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.
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