NousNav: A low-cost neuronavigation system for deployment in lower-resource settings

Léger, Étienne; Horvath, Samantha; Fillion‐Robin, Jean‐Christophe; Allemang, David; Gerber, Samuel; Juvekar, Parikshit; Torio, Erickson; Kapur, Tina; Pieper, Steve; Pujol, Sonia; Bardsley, R.E.; Frisken, Sarah; Golby, Alexandra J.

doi:10.1007/s11548-022-02644-w

Cited by 6 publications

(4 citation statements)

References 11 publications

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“…Low-cost neuronavigation platforms under development, such as NousNav [20], offer another promising avenue for the deployment of this technology to lower-resource settings. The software component for NousNav, being a custom open-source fork of 3D Slicer, improves its potential compatibility with our technique and also eliminates the expensive precondition of purchasing interface licenses (e.g., StealthLink) from commercial vendors.…”

Section: System Compatibility and Generalizabilitymentioning

confidence: 99%

Mapping Resection Progress by Tool-Tip Tracking during Brain Tumor Surgery for Real-Time Estimation of Residual Tumor

Juvekar

Torio

et al. 2023

Cancers

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Surgical resection continues to be the primary initial therapeutic strategy in the treatment of patients with brain tumors. Computerized cranial neuronavigation based on preoperative imaging offers precision guidance during craniotomy and early tumor resection but progressively loses validity with brain shift. Intraoperative MRI (iMRI) and intraoperative ultrasound (iUS) can update the imaging used for guidance and navigation but are limited in terms of temporal and spatial resolution, respectively. We present a system that uses time-stamped tool-tip positions of surgical instruments to generate a map of resection progress with high spatial and temporal accuracy. We evaluate this system and present results from 80 cranial tumor resections. Regions of the preoperative tumor segmentation that are covered by the resection map (True Positive Tracking) and regions of the preoperative tumor segmentation not covered by the resection map (True Negative Tracking) are determined for each case. We compare True Negative Tracking, which estimates the residual tumor, with the actual residual tumor identified using iMRI. We discuss factors that can cause False Positive Tracking and False Negative Tracking, which underestimate and overestimate the residual tumor, respectively. Our method provides good estimates of the residual tumor when there is minimal brain shift, and line-of-sight is maintained. When these conditions are not met, surgeons report that it is still useful for identifying regions of potential residual.

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Section: System Compatibility and Generalizabilitymentioning

confidence: 99%

Mapping Resection Progress by Tool-Tip Tracking during Brain Tumor Surgery for Real-Time Estimation of Residual Tumor

Juvekar

Torio

et al. 2023

Cancers

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“…Every year, approximately 13.8 million patients worldwide require neurosurgery to address brain and central nervous system diseases or injuries 1 . Intraoperative neuronavigation systems have been particularly useful to neurosurgeons to localize their surgical instruments relative to the patient's anatomy, facilitating access to the surgical target while avoiding critical structures 2 . Their application has reduced hospitalization days, severe complication rates, procedure costs, patient discomfort, and recovery time 3 .…”

Section: Introductionmentioning

confidence: 99%

“…Accurate registration is essential to effective intraoperative navigation to prevent incorrect targeting of healthy structures, so errors in skin surface segmentation can be critically detrimental to navigation accuracy. Although commercially available in high-income countries, current neuronavigation systems are not accessible in low-resource settings due to their high cost, yet 82% of neurological conditions requiring neurosurgery occur in low-and middle-income countries (LMICs) 2 . NousNav, a low-cost, portable, open-source neuronavigation system, aims to address this unmet need by democratizing access to such technology that may improve global health.…”

Section: Introductionmentioning

confidence: 99%

Open-source, deep-learning skin surface segmentation model for cost-effective neuronavigation accessible to low-resource settings

Bi,

Pieper,

Chlorogiannis

et al. 2024

Medical Imaging 2024: Image Processing

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Purpose: Neuronavigation has become integral to neurosurgery. By helping neurosurgeons localize their surgical instruments according to the patient's anatomy in real time, neuronavigation facilitates improved patient outcomes and lower procedure costs. This technology has been out of reach of surgeons in lower resource settings due to cost and infrastructure limitations. Our group has developed NousNav, an open-source low-cost navigation system accessible in such environments to address this need. Neuronavigation requires skin surface segmentation from preoperative images, which is necessary for image-to-patient registration. Thus, accurate skin segmentation is essential for effective neuronavigation. However, manual skin surface segmentation is time-consuming, reliant on technical expertise, and susceptible to inter-rater variability. We propose to implement deep learning into the skin surface segmentation step of NousNav, mitigating the limitations of manual segmentation. Approach:We built a nnU-Net model to automate skin segmentation, training it on 36 healthy brain T1-FLASH MRIs and testing it on another 5 healthy brain MRIs. We evaluated the model's performance versus that of three human raters, using Hausdorff distance (in mm) and Dice metrics.Results: On average, comparing the human raters to each other and the automated segmentation model to the human raters yielded Hausdorff distances of 1.3534mm and 1.0198mm and Dice scores of 0.9750 and 0.9820, respectively, demonstrating that automated skin surface segmentation performs comparably to manual segmentation.Conclusions: Here we uniquely implemented automated segmentation for meeting the unmet need of accessible, open-source neuronavigation systems. Considering the promising performance of our deep learning model, this step may be feasibly incorporated into the NousNav workflow.

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“…Subdural hematoma evacuations are often performed without intraoperative image guidance, likely due to setup time, cost of traditional navigation, and various institutional logistics. Surgeons typically rely on the patient’s pre-operative computed tomography (CT) and/or magnetic resonance imaging (MRI) imaging with or without pre-operative fiducial placement [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Technical report: clinical feasibility of augmented reality-navigated chronic subdural hematoma evacuation

Olexa,

Trang,

Chryssikos

et al. 2024

Journal of Surgical Case Reports

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Management of chronic subdural hematoma (cSDH) poses unique challenges and can be fraught with complications. Understanding the spatial relationships of cSDH to adjacent brain tissue and skull topography is critical for successful surgical treatment. The aim of this report is to highlight the feasibility and efficacy of a novel augmented reality (AR) overlay tool for surgical planning with technical description of two surgical cases using AR for surgical management of cSDH. This report describes a fiducial-less AR system for surgical planning of surgical evacuation of cSDH. The AR system was used to superimpose 3D anatomy onto the patients head to provide image guidance during two cases of evacuation. Imaging demonstrated convexity cSDH. A 3D model of the patient’s anatomy was created and registered onto the patients’ heads using a novel AR system. Surgical evacuation of the cSDH was completed in each case with surgical planning assisted by AR overlay.

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NousNav: A low-cost neuronavigation system for deployment in lower-resource settings

Cited by 6 publications

References 11 publications

Mapping Resection Progress by Tool-Tip Tracking during Brain Tumor Surgery for Real-Time Estimation of Residual Tumor

Mapping Resection Progress by Tool-Tip Tracking during Brain Tumor Surgery for Real-Time Estimation of Residual Tumor

Open-source, deep-learning skin surface segmentation model for cost-effective neuronavigation accessible to low-resource settings

Technical report: clinical feasibility of augmented reality-navigated chronic subdural hematoma evacuation

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