Deep learning based Non-Rigid Volume-to-Surface Registration for Brain Shift compensation Using Point Cloud

2026-04-19

AI Summary

This work addresses the challenge of intraoperative brain shift, which compromises neuronavigation accuracy by introducing discrepancies between preoperative images and actual anatomy. Existing compensation methods typically rely on invasive intraoperative volumetric imaging. To overcome this limitation, the authors propose a deep learning–based non-rigid body-to-surface registration framework that recovers a dense whole-brain deformation field using only sparse intraoperative cortical point clouds—without requiring explicit point correspondences or intraoperative volume data. By implicitly integrating local surface observations into the complete preoperative point cloud domain and leveraging multi-scale feature extraction with a hierarchical deformation decoder, the model achieves end-to-end registration. Under partial observation conditions, it attains a target registration error of 1.13 ± 0.75 mm and an RMSE of 1.33 ± 0.81 mm, significantly improving fine-grained deformation estimation and enabling automatic, image-free intraoperative compensation.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Intelligent Robots: Localization, Mapping, and Navigation

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Soft-tissue deformation remains a major limitation in image-guided neurosurgery, where intra-operative anatomy can deviate substantially from pre-operative imaging due to brain shift, compromising navigation accuracy and surgical safety. Existing compensation methods often rely on intra-operative MRI, CT, or ultrasound, which are disruptive and difficult to integrate repeatedly into the surgical workflow. In contrast, partial 3D cortical surfaces can be reconstructed as point clouds from stereoscopic microscopes or laser range scanners (LRS), capturing only a limited portion of the exposed cortex. This makes point cloud registration a practical alternative without interrupting surgery; however, such partial and noisy observations make deformation estimation highly challenging. In this study, we propose a deep learning-based framework for non-rigid volume-to-surface registration, enabling dense displacement field estimation from sparse intra-operative surface observations without explicit point correspondences or volumetric intra-operative imaging. The network leverages multi-scale point-based feature extraction and a hierarchical deformation decoder to capture both global and local deformations. The key contribution lies in integrating partial intra-operative surface information into the full pre-operative point cloud domain, enabling implicit correspondence learning and dense deformation recovery under limited visibility. Quantitative results demonstrate accurate recovery of fine-scale deformations, achieving an Endpoint Error (EPE) of 1.13 +/- 0.75 mm and RMSE of 1.33 +/- 0.81 mm under challenging partial-surface conditions. The proposed approach supports automatic, workflow-compatible brain-shift compensation from sparse surface observations.

Problem

• brain shift
• non-rigid registration
• point cloud
• image-guided neurosurgery
• soft-tissue deformation

Innovation

• non-rigid registration
• point cloud
• brain shift compensation
• deep learning
• volume-to-surface