Establishing dense anatomical correspondence across distinct imaging
modalities is a foundational yet challenging procedure for numerous medical
image analysis studies and image-guided radiotherapy. Existing multi-modality
image registration algorithms rely on statistical-based similarity measures or
local structural image representations. However, the former is sensitive to
locally varying noise, while the latter is not discriminative enough to cope
with complex anatomical structures in multimodal scans, causing ambiguity in
determining the anatomical correspondence across scans with different
modalities. In this paper, we propose a modality-agnostic structural
representation learning method, which leverages Deep Neighbourhood
Self-similarity (DNS) and anatomy-aware contrastive learning to learn
discriminative and contrast-invariance deep structural image representations
(DSIR) without the need for anatomical delineations or pre-aligned training
images. We evaluate our method on multiphase CT, abdomen MR-CT, and brain MR
T1w-T2w registration. Comprehensive results demonstrate that our method is
superior to the conventional local structural representation and
statistical-based similarity measures in terms of discriminability and
accuracy.

基于多模态医学图像分析研究和图像引导放疗，本文提出了一种自主模态结构表示学习方法，利用深度邻域自相似性和解剖感知对比学习，以学习具有辨别性和对比不变性的深度结构图像表示，无需解剖规划或预先对齐训练图像，通过对多相 CT、腹部 MR-CT 和脑部 MR T1w-T2w 进行评估，全面结果表明，相对于传统的局部结构表示和基于统计的相似性度量，所提出的方法在辨别性和准确性上更出色。