Liu Yuchen, Wang Ling, Ning Xiaolin, Gao Yang, Wang Defeng
School of Instrumentation Science and Opto-electronics Engineering, Beihang University, Beijing 100191, China.
Institute of Large-Scale Scientific Facility and Centre for Zero Magnetic Field Science, Beihang University, Beijing 100191, China.
iScience. 2025 Jan 3;28(2):111734. doi: 10.1016/j.isci.2024.111734. eCollection 2025 Feb 21.
Deformable image registration (DIR) is essential for medical image analysis, facilitating the establishment of dense correspondences between images to analyze complex deformations. Traditional registration algorithms often require significant computational resources due to iterative optimization, while deep learning approaches face challenges in managing diverse deformation complexities and task requirements. We introduce ScaMorph, an unsupervised learning model for DIR that employs scale-aware context aggregation, integrating multiscale mixed convolution with lightweight multiscale context fusion. This model effectively combines convolutional networks and vision transformers, addressing various registration tasks. We also present diffeomorphic variants of ScaMorph to maintain topological deformations. Extensive experiments on 3D medical images across five applications-atlas-to-patient and inter-patient brain magnetic resonance imaging (MRI) registration, inter-modal brain MRI registration, inter-patient liver computed tomography (CT) registration as well as inter-modal abdomen MRI-CT registration-demonstrate that our model significantly outperforms existing methods, highlighting its effectiveness and broader implications for enhancing medical image registration techniques.
可变形图像配准（DIR）对于医学图像分析至关重要，它有助于在图像之间建立密集对应关系，以分析复杂变形。传统的配准算法由于迭代优化通常需要大量计算资源，而深度学习方法在处理各种变形复杂性和任务要求方面面临挑战。我们引入了ScaMorph，一种用于DIR的无监督学习模型，它采用尺度感知上下文聚合，将多尺度混合卷积与轻量级多尺度上下文融合相结合。该模型有效地结合了卷积网络和视觉Transformer，可处理各种配准任务。我们还提出了ScaMorph的微分同胚变体，以保持拓扑变形。在五个应用中的3D医学图像上进行的广泛实验——图谱到患者和患者间脑磁共振成像（MRI）配准、模态间脑MRI配准、患者间肝脏计算机断层扫描（CT）配准以及模态间腹部MRI-CT配准——表明我们的模型显著优于现有方法，突出了其在增强医学图像配准技术方面的有效性和更广泛的意义。
