Linearformer: Tri-Net Multi-Layer DVF Medical Image Registration

Abstract

In medical imaging, accurate registration is crucial for reliable analysis. While transformer models demonstrate potential, their application to large datasets like OASIS is constrained by substantial memory requirements, quadratic complexity and the challenge of managing complex deformations. To overcome these challenges, Linearformer is introduced, an efficient transformer-based model with Linear-ProbSparse self-attention for optimised time and memory, along with TNM DVF, a Pyramid-based framework for unsupervised non-rigid registration. Evaluated on OASIS and LPBA40 brain MRI datasets, the model outperforms state-of-the-art methods in Dice score and Jacobian metrics, surpassing TransMatch by 0.6% and 1.9% on the two datasets while maintaining a comparable voxel folding percentage.