Deformable Image Registration with Stochastically Regularized Biomechanical Equilibrium

2023/12/22 by Pablo Álvarez, Alvarez, Pablo, Stéphane Cotin +1 · 1 citation
Computer Science · Engineering · #Advanced Neural Network Applications #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Physical sciences #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #Medical Image Segmentation Techniques #Medical Physics (physics.med-ph) #Robotics and Sensor-Based Localization #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2312.14987

openalex publication_date 2023/12/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01

Abstract

Numerous regularization methods for deformable image registration aim at enforcing smooth transformations, but are difficult to tune-in a priori and lack a clear physical basis. Physically inspired strategies have emerged, offering a sound theoretical basis, but still necessitating complex discretization and resolution schemes. This study introduces a regularization strategy that does not require discretization, making it compatible with current registration frameworks, while retaining the benefits of physically motivated regularization for medical image registration. The proposed method performs favorably in both synthetic and real datasets, exhibiting an accuracy comparable to current state-of-the-art methods.

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• cIDIR: Conditioned Implicit Neural Representation for Regularized Deformable Image Registration

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