arXiv subjects

Houssem-Eddine Gueziri

Publications and source records attributed to Houssem-Eddine Gueziri.

3 recordsLinked to original sources

arXiv2026

Novel Ex-vivo Calf Brain Model with Integrated Sub-Skull Force Sensors to Access Simulated Neurosurgical Procedures

Surgical tissue manipulation demands precision; however, tool-tissue manipulation force magnitudes under realistic conditions are rarely quantified. To address this gap, we proposed and validated a portable ex-vivo force-sensing platform that measures tool-tissue interaction forces across the skull-brain interface during simulated neurosurgery. The system involves fresh calf brain tissue, used as a biological surrogate for brain parenchyma, placed in a 3D-printed human skull model equipped with a 6 degree-of-freedom force/torque sensor and a real-time data acquisition system. Five validation protocols assessed the accuracy and dynamic fidelity of the platform against ground-truth measurement, static accuracy and linearity using calibrated weights (0.5-50 g), minimum detectable force, spatial consistency across different anatomical regions, effect of surgical draping, and long-duration stability. Across protocols, measured forces showed excellent agreement with reference loads (correlation R = 0.9997), with RMSE < 0.005 N and mean relative error under 2%. The platform reliably detected low-magnitude forces down to 1 g (9.8 mN), while surgical drapes introduced no meaningful signal distortion and prolonged recordings exhibited minimal drift. Overall, the proposed framework provides objective, high-fidelity force quantification for skill training and performance assessment using fresh calf brain tissue and may serve as a foundation for force-based evaluation across other surgical procedures. Future work will integrate clinically used surgical instruments to increase procedural realism and will progress toward clinical trials to evaluate usability, educational impact, and translational relevance in practice-adjacent settings.

cs.RO↗

arXiv2025

A High-Fidelity Neurosurgical Training Platform for Bimanual Procedures: A Feasibility Study

Background. Bimanual psychomotor proficiency is fundamental to neurosurgical procedures, yet it remains difficult for trainees to acquire and for educators to objectively evaluate performance. In this study, we investigate the feasibility of a neurosurgical simulation platform that integrates an anatomically realistic brain model with surgical instrument tracking to support training and objective assessment of bimanual tasks in the context of subpial corticectomy. Methods. We developed and evaluated a neurosurgical simulation platform based on an ex-vivo calf brain model and a multi-camera tracking system capable of simultaneously capturing the motion of surgical instruments in both hands, including collection of real-time instrument trajectories and synchronized video recordings. These enabled extraction of motion-based, time-based, and bimanual coordination metrics. We conducted a case series involving 47 participants across four training levels: medical students, junior residents, senior residents, and neurosurgeons. Results. The tracking system successfully captured instrument motion during 81% of the periods when instruments were actively used throughout the simulation procedure. Several extracted metrics were able to significantly differentiate between levels of surgical expertise. In particular, instrument usage duration and custom-defined bimanual coordination metrics such as instrument tip separation distance and simultaneous usage time, show potential as features to identify participant expertise levels with different instruments. Conclusions. We demonstrated the feasibility of tracking surgical instruments during complex bimanual tasks in an ex-vivo brain simulation platform. The metrics developed provide a foundation for objective performance assessment and highlight the potential of motion analysis to improve neurosurgical training and evaluation.

cs.CE↗

arXiv2023

Hessian-based Similarity Metric for Multimodal Medical Image Registration

One of the fundamental elements of both traditional and certain deep learning medical image registration algorithms is measuring the similarity/dissimilarity between two images. In this work, we propose an analytical solution for measuring similarity between two different medical image modalities based on the Hessian of their intensities. First, assuming a functional dependence between the intensities of two perfectly corresponding patches, we investigate how their Hessians relate to each other. Secondly, we suggest a closed-form expression to quantify the deviation from this relationship, given arbitrary pairs of image patches. We propose a geometrical interpretation of the new similarity metric and an efficient implementation for registration. We demonstrate the robustness of the metric to intensity nonuniformities using synthetic bias fields. By integrating the new metric in an affine registration framework, we evaluate its performance for MRI and ultrasound registration in the context of image-guided neurosurgery using target registration error and computation time.

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