Deep Biomechanically-Guided Interpolation for Keypoint-Based Brain Shift Registration

2025-08-19

AI Summary

In neurosurgical navigation, landmark-based registration methods exhibit robustness to large deformations but rely on geometric interpolation to generate displacement fields, neglecting the biomechanical properties of brain tissue. This work proposes a biomechanics-guided deep learning framework: a residual 3D U-Net is trained on physically plausible deformations synthesized via biomechanical simulation, refining conventional interpolation-based displacement fields. To our knowledge, this is the first approach to embed physical interpretability into dense deformation estimation within landmark-based registration, enabling efficient reconstruction of biomechanically consistent dense displacement fields from sparse landmarks. In large-scale simulation experiments, the method reduces mean squared error by 50% compared to classical interpolation methods, with negligible inference overhead. It thus significantly improves both registration accuracy and clinical applicability.

Technology Category

• Machine Learning: Neuro-Symbolic Learning
• Intelligent Robots: Localization, Mapping, and Navigation
• Computer Vision: Low Level & Physics-based Vision

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Accurate compensation of brain shift is critical for maintaining the reliability of neuronavigation during neurosurgery. While keypoint-based registration methods offer robustness to large deformations and topological changes, they typically rely on simple geometric interpolators that ignore tissue biomechanics to create dense displacement fields. In this work, we propose a novel deep learning framework that estimates dense, physically plausible brain deformations from sparse matched keypoints. We first generate a large dataset of synthetic brain deformations using biomechanical simulations. Then, a residual 3D U-Net is trained to refine standard interpolation estimates into biomechanically guided deformations. Experiments on a large set of simulated displacement fields demonstrate that our method significantly outperforms classical interpolators, reducing by half the mean square error while introducing negligible computational overhead at inference time. Code available at: href{https://github.com/tiago-assis/Deep-Biomechanical-Interpolator}{https://github.com/tiago-assis/Deep-Biomechanical-Interpolator}.

Problem

• Compensating brain shift during neurosurgery accurately
• Improving keypoint-based registration with biomechanical guidance
• Generating dense physically plausible deformations from sparse keypoints

Innovation

• Deep learning framework for biomechanically guided brain registration
• Residual 3D U-Net refines standard interpolation with biomechanics
• Generates synthetic training data through biomechanical simulations