面向可变形多模态医学图像配准的模态无关结构图像表示学习
Modality-Agnostic Structural Image Representation Learning for Deformable Multi-Modality Medical Image Registration
AI总结：
本文提出模态无关的结构表示学习方法，结合深度邻域自相似性和解剖感知对比学习，在无需标注或预对齐的情况下学习判别性深度结构表示，并在多期相CT、腹部MR-CT和脑MR配准中取得优于传统方法的精度。
摘要：
在不同成像模态之间建立密集的解剖对应关系，是众多医学图像分析研究和图像引导放疗中一项基础但具有挑战性的任务。现有的多模态图像配准算法依赖于基于统计的相似性度量或局部结构图像表示。然而，前者对局部变化的噪声敏感，而后者在应对多模态扫描中的复杂解剖结构时判别力不足，导致在确定不同模态扫描之间的解剖对应关系时产生歧义。本文提出了一种模态无关的结构表示学习方法，该方法利用深度邻域自相似性（DNS）和解剖感知对比学习，在无需解剖标注或预对齐训练图像的情况下，学习具有判别性和对比不变性的深度结构图像表示（DSIR）。我们在多期相CT、腹部MR-CT以及脑MR T1w-T2w配准任务上对所提方法进行了评估。综合实验结果表明，在判别力和配准精度方面，我们的方法优于传统的局部结构表示和基于统计的相似性度量。
英文摘要：
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.
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