用体素自适应消息传递提升腹腔CT变形配准的精度与速度
VoxelOpt: Voxel-Adaptive Message Passing for Discrete Optimization in Deformable Abdominal CT Registration
- 基于体素熵动态调整消息传递强度，弱信号体素受邻域影响更小
- 多层级图像金字塔结合27邻域代价体积，效率远超传统方法
- 采用预训练分割模型提取特征，无需人工设计或对比学习
近期神经网络发展通过摊销迭代优化提升了可变形图像配准（DIR）的速度与精度，但基于学习的方法在训练数据有限、形变过大时表现不佳，且无标签监督下性能常逊于迭代方法。尽管迭代方法精度更高，但运行时间显著更长。为此，本文提出VoxelOpt，一种基于离散优化的DIR框架，融合学习与迭代方法优势，实现精度与速度的更好平衡。VoxelOpt利用局部代价体积中的位移熵衡量每个体素的位移信号强度，相较以往方法有三点改进：第一，引入体素自适应消息传递，熵较低的体素受邻域影响更小；第二，采用多层级图像金字塔与每层27邻域代价体积，避免复杂度指数增长；第三，用预训练基础分割模型替代手工特征或对比学习进行特征提取。在腹部CT配准中，VoxelOpt在效率和精度上均优于领先迭代方法，并达到有标签监督下最先进学习方法的水平。代码将开源。
原文摘要 · Abstract (English)
Recent developments in neural networks have improved deformable image registration (DIR) by amortizing iterative optimization, enabling fast and accurate DIR results. However, learning-based methods often face challenges with limited training data, large deformations, and tend to underperform compared to iterative approaches when label supervision is unavailable. While iterative methods can achieve higher accuracy in such scenarios, they are considerably slower than learning-based methods. To address these limitations, we propose VoxelOpt, a discrete optimization-based DIR framework that combines the strengths of learning-based and iterative methods to achieve a better balance between registration accuracy and runtime. VoxelOpt uses displacement entropy from local cost volumes to measure displacement signal strength at each voxel, which differs from earlier approaches in three key aspects. First, it introduces voxel-wise adaptive message passing, where voxels with lower entropy receives less influence from their neighbors. Second, it employs a multi-level image pyramid with 27-neighbor cost volumes at each level, avoiding exponential complexity growth. Third, it replaces hand-crafted features or contrastive learning with a pretrained foundational segmentation model for feature extraction. In abdominal CT registration, these changes allow VoxelOpt to outperform leading iterative in both efficiency and accuracy, while matching state-of-the-art learning-based methods trained with label supervision. The source code will be available at https://github.com/tinymilky/VoxelOpt
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。