提出自适应卷积块，提升医学图像配准对空间变化信息的捕捉能力。
SACB-Net: Spatial-awareness Convolutions for Medical Image Registration
- 通过特征相似性聚类生成自适应卷积核，响应空间变化
- 在脑部和腹部CT数据集上达到领先性能，尤其擅长大形变配准
- 适合需要高精度医学图像配准的研究者与临床应用
基于深度学习的图像配准方法虽已取得顶尖性能与快速推理速度，但多数方法依赖共享卷积核，难以捕捉特征图中非局部区域的空间变化信息，导致形变场估计不理想。本文提出3D空间感知卷积块（SACB），通过特征相似性识别特征图中的空间聚类，并自适应地参数化不同区域的卷积核。该机制可生成随空间变化定制的卷积权重与偏置，有效增强特征表示的空间感知能力。基于SACB构建金字塔形变估计算法（SACB-Net），实现多尺度流组合，特别适用于大形变场景。在脑部IXI、LPBA及腹部CT数据集上的实验表明，SACB有效提升配准精度，SACB-Net优于现有最先进学习型方法。代码开源：https://github.com/x-xc/SACB_Net。
原文摘要 · Abstract (English)
Deep learning-based image registration methods have shown state-of-the-art performance and rapid inference speeds. Despite these advances, many existing approaches fall short in capturing spatially varying information in non-local regions of feature maps due to the reliance on spatially-shared convolution kernels. This limitation leads to suboptimal estimation of deformation fields. In this paper, we propose a 3D Spatial-Awareness Convolution Block (SACB) to enhance the spatial information within feature representations. Our SACB estimates the spatial clusters within feature maps by leveraging feature similarity and subsequently parameterizes the adaptive convolution kernels across diverse regions. This adaptive mechanism generates the convolution kernels (weights and biases) tailored to spatial variations, thereby enabling the network to effectively capture spatially varying information. Building on SACB, we introduce a pyramid flow estimator (named SACB-Net) that integrates SACBs to facilitate multi-scale flow composition, particularly addressing large deformations. Experimental results on the brain IXI and LPBA datasets as well as Abdomen CT datasets demonstrate the effectiveness of SACB and the superiority of SACB-Net over the state-of-the-art learning-based registration methods. The code is available at https://github.com/x-xc/SACB_Net .
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