用嵌套注意力提升医学影像非线性配准精度
NestedMorph: Enhancing Deformable Medical Image Registration with Nested Attention Mechanisms
- 采用多尺度嵌套注意力融合，结合高低分辨率特征
- 在HCP数据集上达0.89的最高SSIM与2.5的最低HD95
- 适合需要高精度配准的脑影像研究与临床应用
可变形图像配准对于跨模态医学图像的非线性对齐至关重要，能实现不同解剖结构间的精确空间对应。本文提出NestedMorph，一种基于嵌套注意力融合机制的新网络，用于提高T1加权磁共振（T1w MRI）与扩散MRI（dMRI）数据间同一受试者的可变形配准性能。该模型通过多尺度框架，将编码器提供的高分辨率空间细节与解码器的语义信息融合，增强局部与全局特征提取能力。在HCP数据集上的评估显示，NestedMorph显著优于现有方法，包括基于CNN的VoxelMorph、MIDIR、CycleMorph，基于Transformer的TransMorph与ViT-V-Net，以及传统方法NiftyReg与SyN。关键指标表现优异：最高SSIM达0.89，最低HD95为2.5，SDlogJ为0.22。结果表明，NestedMorph能有效捕捉局部与全局图像特征，实现更优配准效果。本研究为可变形医学图像配准提供了强有力框架，具有重要临床与科研价值。源代码与实现见：https://github.com/AS-Lab/Marthi-et-al-2024-NestedMorph-Deformable-Medical-Image-Registration
原文摘要 · Abstract (English)
Deformable image registration is crucial for aligning medical images in a nonlinear fashion across different modalities, allowing for precise spatial correspondence between varying anatomical structures. This paper presents NestedMorph, a novel network utilizing a Nested Attention Fusion approach to improve intra-subject deformable registration between T1-weighted (T1w) MRI and diffusion MRI (dMRI) data. NestedMorph integrates high-resolution spatial details from an encoder with semantic information from a decoder using a multi-scale framework, enhancing both local and global feature extraction. Our model notably outperforms existing methods, including CNN-based approaches like VoxelMorph, MIDIR, and CycleMorph, as well as Transformer-based models such as TransMorph and ViT-V-Net, and traditional techniques like NiftyReg and SyN. Evaluations using the HCP dataset demonstrate that NestedMorph achieves superior performance across key metrics, including SSIM, HD95, and SDlogJ, with the highest SSIM of 0.89, the lowest HD95 of 2.5 and SDlogJ of 0.22. These results highlight NestedMorph's ability to capture both local and global image features effectively, leading to superior registration performance. The promising outcomes of this study underscore NestedMorph's potential to significantly advance deformable medical image registration, providing a robust framework for future research and clinical applications. The source code and our implementation are available at: https://github.com/AS-Lab/Marthi-et-al-2024-NestedMorph-Deformable-Medical-Image-Registration
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