Medical brain imaging relies heavily on image registration to accurately curate structural boundaries of brain features for various healthcare applications. Deep learning models have shown remarkable performance in image registration in recent years. Still, they often struggle to handle the diversity of 3D brain volumes, challenged by their structural and contrastive variations and their imaging domains. In this work, we present NeuReg, a Neuro-inspired 3D image registration architecture with the feature of domain invariance. NeuReg generates domain-agnostic representations of imaging features and incorporates a shifting window-based Swin Transformer block as the encoder. This enables our model to capture the variations across brain imaging modalities and species. We demonstrate a new benchmark in multi-domain publicly available datasets comprising human and mouse 3D brain volumes. Extensive experiments reveal that our model (NeuReg) outperforms the existing baseline deep learning-based image registration models and provides a high-performance boost on cross-domain datasets, where models are trained on 'source-only' domain and tested on completely 'unseen' target domains. Our work establishes a new state-of-the-art for domain-agnostic 3D brain image registration, underpinned by Neuro-inspired Transformer-based architecture.

翻译：医学脑成像高度依赖图像配准技术，以精准界定脑部特征的结构边界，服务于各类医疗健康应用。近年来，深度学习模型在图像配准领域展现出卓越性能。然而，面对三维脑体积数据的多样性——包括其结构差异、对比度变化及成像领域的不同——现有模型往往难以有效处理。本研究提出NeuReg，一种受神经启发的、具备领域不变特性的三维图像配准架构。NeuReg能够生成与成像领域无关的特征表示，并采用基于滑动窗口的Swin Transformer模块作为编码器。这使得我们的模型能够捕捉跨脑成像模态及物种的变异。我们在包含人类与小鼠三维脑体积的公开多领域数据集上建立了新的基准。大量实验表明，我们的模型（NeuReg）超越了现有的基于深度学习的图像配准基线模型，并在跨领域数据集上实现了显著的性能提升——该场景下模型仅在“源”领域训练，而在完全“未见”的目标领域测试。我们的工作依托于受神经启发的Transformer架构，为领域无关的三维脑图像配准确立了新的技术标杆。