J Neurosurg 144:206–216, 2026
This study presents a fully automated intraoperative image-updating system that compensates for brain shift after dural opening by assimilating intraoperative stereovision (iSV) data into a biomechanical finite element model to deform preoperative MR (pMR) images into updated MR (uMR) images. The pipeline integrates FastSAM segmentation, vessel/sulcus feature registration, and FEM-based whole-brain deformation to produce clinically usable uMRs.
In fifteen open cranial cases, automated updates reduced mean target registration error from 6.2 mm (pMR) to 1.9 mm (uMR) and completed without user intervention in 3.9 ± 0.6 minutes. Results demonstrate robust segmentation (DSC 0.93), submillimeter iSV reconstruction accuracy, and potential for broader adoption, while noting limitations in small or featureless openings and need for deeper-structure validation.
Fully automated image updating: Developed and validated a fully automated system to update preoperative MRI images for brain shift compensation after dural opening in open cranial surgery, eliminating user intervention and expertise requirements.
Intraoperative stereovision (iSV) integration: Utilized iSV images to capture high-resolution surface deformation data, which was processed by deep learning-based segmentation (FastSAM) and registered with preoperative MRI using vessel and sulcus features.
Two-step registration process: Employed translation-only cross-correlation for global alignment and Demons deformable registration for local nonrigid deformation between iSV and preoperative MRI surfaces.
Biomechanical modeling: Assimilated extracted nonrigid cortical displacements into a finite element model to estimate whole-brain deformation and generate updated MR images (uMR).
Accuracy improvement: Achieved significant reduction in target registration error (TRE) from 6.2 ± 1.2 mm (pMR) to 1.9 ± 1.0 mm (uMR), with overall mean computational time of 3.9 ± 0.6 minutes and no user intervention.
Robustness and efficiency: System was robust across a range of surgical conditions (lesion type, craniotomy size, brain shift magnitude), and performance was not significantly affected by these variables.
Limitations: Current system requires clear iSV images (free of instruments/blood), is limited to initial post-dural opening updates, and surface accuracy was primarily evaluated; further development is needed for autonomous updates during resection and deeper structure validation.
Potential for broad adoption: Elimination of user dependency and minimal workflow interruption suggest strong potential for integration into routine open cranial







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