Data-Driven Tissue- and Subject-Specific Elastic Regularization for Medical Image Registration
This work addresses the problem of improving registration accuracy for medical imaging applications, particularly in intra-patient scenarios, by enabling patient-specific parameter estimation without retraining, though it is incremental as it builds on existing physics-inspired regularization methods.
The paper tackled the challenge of subject-specific elasticity parameter estimation in medical image registration by introducing a data-driven method using hypernetworks to learn tissue-dependent parameters, achieving higher registration quality across lung CT and cardiac MR datasets compared to global regularizers.
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.