EfficientMorph: Parameter-Efficient Transformer-Based Architecture for 3D Image Registration

• URL: http://arxiv.org/abs/2403.11026v1
• Date: Sat, 16 Mar 2024 22:01:55 GMT
• Title: EfficientMorph: Parameter-Efficient Transformer-Based Architecture for 3D Image Registration
• Authors: Abu Zahid Bin Aziz, Mokshagna Sai Teja Karanam, Tushar Kataria, Shireen Y. Elhabian,
• Abstract summary: We propose EfficientMorph, a transformer-based architecture for unsupervised 3D image registration. It optimize the balance between local and global attention through a plane-based attention mechanism. It reduces computational redundancy via cascaded group attention, and captures fine details without compromising computational efficiency.
• Score: 1.741980945827445
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Transformers have emerged as the state-of-the-art architecture in medical image registration, outperforming convolutional neural networks (CNNs) by addressing their limited receptive fields and overcoming gradient instability in deeper models. Despite their success, transformer-based models require substantial resources for training, including data, memory, and computational power, which may restrict their applicability for end users with limited resources. In particular, existing transformer-based 3D image registration architectures face three critical gaps that challenge their efficiency and effectiveness. Firstly, while mitigating the quadratic complexity of full attention by focusing on local regions, window-based attention mechanisms often fail to adequately integrate local and global information. Secondly, feature similarities across attention heads that were recently found in multi-head attention architectures indicate a significant computational redundancy, suggesting that the capacity of the network could be better utilized to enhance performance. Lastly, the granularity of tokenization, a key factor in registration accuracy, presents a trade-off; smaller tokens improve detail capture at the cost of higher computational complexity, increased memory demands, and a risk of overfitting. Here, we propose EfficientMorph, a transformer-based architecture for unsupervised 3D image registration. It optimizes the balance between local and global attention through a plane-based attention mechanism, reduces computational redundancy via cascaded group attention, and captures fine details without compromising computational efficiency, thanks to a Hi-Res tokenization strategy complemented by merging operations. Notably, EfficientMorph sets a new benchmark for performance on the OASIS dataset with 16-27x fewer parameters.

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