arXiv 2407.043552024-07-05cs.CV

数据驱动的组织与受试者特异性弹性正则化用于医学图像配准

Data-Driven Tissue- and Subject-Specific Elastic Regularization for Medical Image Registration

School of Computation, Information and Technology, Technical University of Munich（慕尼黑工业大学计算、信息与技术学院）；Munich Center for Machine Learning（慕尼黑机器学习中心）；Institute of Machine Learning in Biomedical Imaging, Helmholtz Munich（亥姆霍兹慕尼黑生物医学成像机器学习研究所）；German Cancer Consortium (DKTK)（德国癌症联盟）；School of Biomedical Engineering and Imaging Sciences, King’s College London（伦敦国王学院生物医学工程与成像科学学院）

Anna Reithmeir, Lina Felsner, Rickmer Braren, Julia A. Schnabel, Veronika A. Zimmer

备注：: Accepted at MICCAI 2024

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AI总结：

本文提出利用超网络数据驱动地学习组织依赖性弹性参数，实现无需重训的患者特异性医学图像配准正则化，并在多个肺CT和心脏MR数据集上验证了其优于全局正则化器的配准质量。

摘要：

受物理启发的正则化在患者内图像配准中是理想的，因为它能够有效捕捉解剖结构的生物力学特性。然而，一个主要挑战在于对物理参数的依赖：文献中的参数估计差异很大，且物理属性本身固有地具有受试者特异性。在这项工作中，我们引入了一种新颖的数据驱动方法，利用超网络来学习弹性正则化器的组织依赖性弹性参数。值得注意的是，我们的方法有助于估计患者特异性参数，而无需重新训练网络。我们在三个公开可用的2D和3D肺部CT及心脏MR数据集上评估了我们的方法。我们发现，与使用全局正则化器相比，我们提出的受试者特异性组织依赖性正则化在所有数据集上都实现了更高的配准质量。代码可在 https://github.com/compai-lab/2024-miccai-reithmeir 获取。

英文摘要：

Physics-inspired regularization is desired for intra-patient image registration since it can effectively capture the biomechanical characteristics of anatomical structures. However, a major challenge lies in the reliance on physical parameters: Parameter estimations vary widely across the literature, and the physical properties themselves are inherently subject-specific. In this work, we introduce a novel data-driven method that leverages hypernetworks to learn the tissue-dependent elasticity parameters of an elastic regularizer. Notably, our approach facilitates the estimation of patient-specific parameters without the need to retrain the network. We evaluate our method on three publicly available 2D and 3D lung CT and cardiac MR datasets. We find that with our proposed subject-specific tissue-dependent regularization, a higher registration quality is achieved across all datasets compared to using a global regularizer. The code is available at https://github.com/compai-lab/2024-miccai-reithmeir.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。