bluuebunny/crossref_metadata_2025_split huggingface dataset:
Virtual dissection of the real brain: integration of photographic 3D models into virtual reality and its effect on neurosurgical resident education
Authors: Roh Tae Hoon , Oh Ji Woong , Jang Chang Ki , Choi Seonah , Kim Eui Hyun , Hong Chang-Ki , Kim Se-Hyuk 
Published: August 2021

OBJECTIVE
Virtual reality (VR) is increasingly being used for education and surgical simulation in neurosurgery. So far, the 3D sources for VR simulation have been derived from medical images, which lack real color. The authors made photographic 3D models from dissected cadavers and integrated them into the VR platform. This study aimed to introduce a method of developing a photograph-integrated VR and to evaluate the educational effect of these models.

METHODS
A silicone-injected cadaver head was prepared. A CT scan of the specimen was taken, and the soft tissue and skull were segmented to 3D objects. The cadaver was dissected layer by layer, and each layer was 3D scanned by a photogrammetric method. The objects were imported to a free VR application and layered. Using the head-mounted display and controllers, the various neurosurgical approaches were demonstrated to neurosurgical residents. After performing hands-on virtual surgery with photographic 3D models, a feedback survey was collected from 31 participants.

RESULTS
Photographic 3D models were seamlessly integrated into the VR platform. Various skull base approaches were successfully performed with photograph-integrated VR. During virtual dissection, the landmark anatomical structures were identified based on their color and shape. Respondents rated a higher score for photographic 3D models than for conventional 3D models (4.3 ± 0.8 vs 3.2 ± 1.1, respectively; p = 0.001). They responded that performing virtual surgery with photographic 3D models would help to improve their surgical skills and to develop and study new surgical approaches.

CONCLUSIONS
The authors introduced photographic 3D models to the virtual surgery platform for the first time. Integrating photographs with the 3D model and layering technique enhanced the educational effect of the 3D models. In the future, as computer technology advances, more realistic simulations will be possible.
Fully automatic brain tumor segmentation for 3D evaluation in augmented reality
Authors: Fick Tim , van Doormaal Jesse A. M. , Tosic Lazar , van Zoest Renate J. , Meulstee Jene W. , Hoving Eelco W. , van Doormaal Tristan P. C. 
Published: August 2021

OBJECTIVE
For currently available augmented reality workflows, 3D models need to be created with manual or semiautomatic segmentation, which is a time-consuming process. The authors created an automatic segmentation algorithm that generates 3D models of skin, brain, ventricles, and contrast-enhancing tumor from a single T1-weighted MR sequence and embedded this model into an automatic workflow for 3D evaluation of anatomical structures with augmented reality in a cloud environment. In this study, the authors validate the accuracy and efficiency of this automatic segmentation algorithm for brain tumors and compared it with a manually segmented ground truth set.

METHODS
Fifty contrast-enhanced T1-weighted sequences of patients with contrast-enhancing lesions measuring at least 5 cm3 were included. All slices of the ground truth set were manually segmented. The same scans were subsequently run in the cloud environment for automatic segmentation. Segmentation times were recorded. The accuracy of the algorithm was compared with that of manual segmentation and evaluated in terms of Sørensen-Dice similarity coefficient (DSC), average symmetric surface distance (ASSD), and 95th percentile of Hausdorff distance (HD95).

RESULTS
The mean ± SD computation time of the automatic segmentation algorithm was 753 ± 128 seconds. The mean ± SD DSC was 0.868 ± 0.07, ASSD was 1.31 ± 0.63 mm, and HD95 was 4.80 ± 3.18 mm. Meningioma (mean 0.89 and median 0.92) showed greater DSC than metastasis (mean 0.84 and median 0.85). Automatic segmentation had greater accuracy for measuring DSC (mean 0.86 and median 0.87) and HD95 (mean 3.62 mm and median 3.11 mm) of supratentorial metastasis than those of infratentorial metastasis (mean 0.82 and median 0.81 for DSC; mean 5.26 mm and median 4.72 mm for HD95).

CONCLUSIONS
The automatic cloud-based segmentation algorithm is reliable, accurate, and fast enough to aid neurosurgeons in everyday clinical practice by providing 3D augmented reality visualization of contrast-enhancing intracranial lesions measuring at least 5 cm3. The next steps involve incorporation of other sequences and improving accuracy with 3D fine-tuning in order to expand the scope of augmented reality workflow.
Evaluation of the effect of standard neuronavigation and augmented reality on the integrity of the perifocal structures during a neurosurgical approach
Authors: Davidovic Alioucha , Chavaz Lara , Meling Torstein R. , Schaller Karl , Bijlenga Philippe , Haemmerli Julien 
Published: August 2021

OBJECTIVE
Intracranial minimally invasive procedures imply working in a restricted surgical corridor surrounded by critical structures, such as vessels and cranial nerves. Any damage to them may affect patient outcome. Neuronavigation systems may reduce the risk of such complications. In this study, the authors sought to compare standard neuronavigation (NV) and augmented reality (AR)–guided navigation with respect to the integrity of the perifocal structures during a neurosurgical approach using a novel model imitating intracranial vessels.

METHODS
A custom-made box, containing crisscrossing hard metal wires, a hidden nail at its bottom, and a wooden top, was scanned, fused, and referenced for the purpose of the study. The metal wires and an aneurysm clip applier were connected to a controller, which counted the number of contacts between them. Twenty-three naive participants were asked to 1) use NV to define an optimal entry point on the top, perform the smallest craniotomy possible on the wooden top, and to use a surgical microscope when placing a clip on the nail without touching the metal wires; and 2) use AR to preoperatively define an ideal trajectory, navigate the surgical microscope, and then perform the same task. The primary outcome was the number of contacts made between the metal wires and the clip applier. Secondary outcomes were craniotomy size, and trust in NV and AR to help avoid touching the metal wires, as assessed by a 9-level Likert scale.

RESULTS
The median number of contacts tended to be lower with the use of AR than with NV (AR, median 1 [Q1: 1, Q3: 2]; NV, median 3 [Q1: 1, Q3: 6]; p = 0.074). The size of the target-oriented craniotomy was significantly lower with the use of AR compared with NV (AR, median 4.91 cm2 [Q1: 4.71 cm2, Q3: 7.55 cm2]; and NV, median 9.62 cm2 [Q1: 7.07 cm2; Q3: 13.85 cm2]). Participants had more trust in AR than in NV (the differences posttest minus pretest were mean 0.9 [SD 1.2] and mean −0.3 [SD 0.2], respectively; p < 0.05).

CONCLUSIONS
The results of this study show a trend favoring the use of AR over NV with respect to reducing contact between a clip applier and the perifocal structures during a simulated clipping of an intracranial aneurysm. Target-guided craniotomies were smaller with the use of AR. AR may be used not only to localize surgical targets but also to prevent complications associated with damage to structures encountered during the surgical approach.
Three-dimensional–printed marker–based augmented reality neuronavigation: a new neuronavigation technique
Authors: Yavas Gorkem , Caliskan Kadri Emre , Cagli Mehmet Sedat 
Published: August 2021

OBJECTIVE
The aim of this study was to assess the precision and feasibility of 3D-printed marker–based augmented reality (AR) neurosurgical navigation and its use intraoperatively compared with optical tracking neuronavigation systems (OTNSs).

METHODS
Three-dimensional–printed markers for CT and MRI and intraoperative use were applied with mobile devices using an AR light detection and ranging (LIDAR) camera. The 3D segmentations of intracranial tumors were created with CT and MR images, and preoperative registration of the marker and pathology was performed. A patient-specific, surgeon-facilitated mobile application was developed, and a mobile device camera was used for neuronavigation with high accuracy, ease, and cost-effectiveness. After accuracy values were preliminarily assessed, this technique was used intraoperatively in 8 patients.

RESULTS
The mobile device LIDAR camera was found to successfully overlay images of virtual tumor segmentations according to the position of a 3D-printed marker. The targeting error that was measured ranged from 0.5 to 3.5 mm (mean 1.70 ± 1.02 mm, median 1.58 mm). The mean preoperative preparation time was 35.7 ± 5.56 minutes, which is longer than that for routine OTNSs, but the amount of time required for preoperative registration and the placement of the intraoperative marker was very brief compared with other neurosurgical navigation systems (mean 1.02 ± 0.3 minutes).

CONCLUSIONS
The 3D-printed marker–based AR neuronavigation system was a clinically feasible, highly precise, low-cost, and easy-to-use navigation technique. Three-dimensional segmentation of intracranial tumors was targeted on the brain and was clearly visualized from the skin incision to the end of surgery.
Real-time navigation guidance with intraoperative CT imaging for pedicle screw placement using an augmented reality head-mounted display: a proof-of-concept study
Authors: Yanni Daniel S. , Ozgur Burak M. , Louis Robert G. , Shekhtman Yevgenia , Iyer Rajiv R. , Boddapati Venkat , Iyer Asha , Patel Purvee D. , Jani Raja , Cummock Matthew , Herur-Raman Aalap , Dang Phuong , Goldstein Ira M. , Brant-Zawadzki Michael , Steineke Thomas , Lenke Lawrence G. 
Published: August 2021

OBJECTIVE
Augmented reality (AR) has the potential to improve the accuracy and efficiency of instrumentation placement in spinal fusion surgery, increasing patient safety and outcomes, optimizing ergonomics in the surgical suite, and ultimately lowering procedural costs. The authors sought to describe the use of a commercial prototype Spine AR platform (SpineAR) that provides a commercial AR head-mounted display (ARHMD) user interface for navigation-guided spine surgery incorporating real-time navigation images from intraoperative imaging with a 3D-reconstructed model in the surgeon's field of view, and to assess screw placement accuracy via this method.

METHODS
Pedicle screw placement accuracy was assessed and compared with literature-reported data of the freehand (FH) technique. Accuracy with SpineAR was also compared between participants of varying spine surgical experience. Eleven operators without prior experience with AR-assisted pedicle screw placement took part in the study: 5 attending neurosurgeons and 6 trainees (1 neurosurgical fellow, 1 senior orthopedic resident, 3 neurosurgical residents, and 1 medical student). Commercially available 3D-printed lumbar spine models were utilized as surrogates of human anatomy. Among the operators, a total of 192 screws were instrumented bilaterally from L2–5 using SpineAR in 24 lumbar spine models. All but one trainee also inserted 8 screws using the FH method. In addition to accuracy scoring using the Gertzbein-Robbins grading scale, axial trajectory was assessed, and user feedback on experience with SpineAR was collected.

RESULTS
Based on the Gertzbein-Robbins grading scale, the overall screw placement accuracy using SpineAR among all users was 98.4% (192 screws). Accuracy for attendings and trainees was 99.1% (112 screws) and 97.5% (80 screws), respectively. Accuracy rates were higher compared with literature-reported lumbar screw placement accuracy using FH for attendings (99.1% vs 94.32%; p = 0.0212) and all users (98.4% vs 94.32%; p = 0.0099). The percentage of total inserted screws with a minimum of 5° medial angulation was 100%. No differences were observed between attendings and trainees or between the two methods. User feedback on SpineAR was generally positive.

CONCLUSIONS
Screw placement was feasible and accurate using SpineAR, an ARHMD platform with real-time navigation guidance that provided a favorable surgeon-user experience.
A systematic review of virtual reality for the assessment of technical skills in neurosurgery
Authors: Chan Justin , Pangal Dhiraj J. , Cardinal Tyler , Kugener Guillaume , Zhu Yichao , Roshannai Arman , Markarian Nicholas , Sinha Aditya , Anandkumar Anima , Hung Andrew , Zada Gabriel , Donoho Daniel A. 
Published: August 2021

OBJECTIVE
Virtual reality (VR) and augmented reality (AR) systems are increasingly available to neurosurgeons. These systems may provide opportunities for technical rehearsal and assessments of surgeon performance. The assessment of neurosurgeon skill in VR and AR environments and the validity of VR and AR feedback has not been systematically reviewed.

METHODS
A systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines was conducted through MEDLINE and PubMed. Studies published in English between January 1990 and February 2021 describing the use of VR or AR to quantify surgical technical performance of neurosurgeons without the use of human raters were included. The types and categories of automated performance metrics (APMs) from each of these studies were recorded.

RESULTS
Thirty-three VR studies were included in the review; no AR studies met inclusion criteria. VR APMs were categorized as either distance to target, force, kinematics, time, blood loss, or volume of resection. Distance and time were the most well-studied APM domains, although all domains were effective at differentiating surgeon experience levels. Distance was successfully used to track improvements with practice. Examining volume of resection demonstrated that attending surgeons removed less simulated tumor but preserved more normal tissue than trainees. More recently, APMs have been used in machine learning algorithms to predict level of training with a high degree of accuracy. Key limitations to enhanced-reality systems include limited AR usage for automated surgical assessment and lack of external and longitudinal validation of VR systems.

CONCLUSIONS
VR has been used to assess surgeon performance across a wide spectrum of domains. The VR environment can be used to quantify surgeon performance, assess surgeon proficiency, and track training progression. AR systems have not yet been used to provide metrics for surgeon performance assessment despite potential for intraoperative integration. VR-based APMs may be especially useful for metrics that are difficult to assess intraoperatively, including blood loss and extent of resection.
Randomized study comparing 3D virtual reality and conventional 2D on-screen teaching of cerebrovascular anatomy
Authors: Greuter Ladina , De Rosa Adriana , Cattin Philippe , Croci Davide Marco , Soleman Jehuda , Guzman Raphael 
Published: August 2021

OBJECTIVE
Performing aneurysmal clipping requires years of training to successfully understand the 3D neurovascular anatomy. This training has traditionally been obtained by learning through observation. Currently, with fewer operative aneurysm clippings, stricter work-hour regulations, and increased patient safety concerns, novel teaching methods are required for young neurosurgeons. Virtual-reality (VR) models offer the opportunity to either train a specific surgical skill or prepare for an individual surgery. With this study, the authors aimed to compare the spatial orientation between traditional 2D images and 3D VR models in neurosurgical residents or medical students.

METHODS
Residents and students were each randomly assigned to describe 4 aneurysm cases, which could be either 2D images or 3D VR models. The time to aneurysm detection as well as a spatial anatomical description was assessed via an online questionnaire and compared between the groups. The aneurysm cases were 10 selected patient cases treated at the authors’ institution.

RESULTS
Overall, the time to aneurysm detection was shorter in the 3D VR model compared to 2D images, with a trend toward statistical significance (25.77 ± 37.26 vs 45.70 ± 51.94 seconds, p = 0.052). No significant difference was observed for residents (3D VR 24.47 ± 40.16 vs 2D 33.52 ± 56.06 seconds, p = 0.564), while in students a significantly shorter time to aneurysm detection was measured using 3D VR models (26.95 ± 35.39 vs 59.16 ± 44.60 seconds, p = 0.015). No significant differences between the modalities for anatomical and descriptive spatial mistakes were observed. Most participants (90%) preferred the 3D VR models for aneurysm detection and description, and only 1 participant (5%) described VR-related side effects such as dizziness or nausea.

CONCLUSIONS
VR platforms facilitate aneurysm recognition and understanding of its spatial anatomy, which could make them the preferred method compared to 2D images in the years to come.
The effect of augmented reality on the accuracy and learning curve of external ventricular drain placement
Authors: Van Gestel Frederick , Frantz Taylor , Vannerom Cédric , Verhellen Anouk , Gallagher Anthony G. , Elprama Shirley A. , Jacobs An , Buyl Ronald , Bruneau Michaël , Jansen Bart , Vandemeulebroucke Jef , Scheerlinck Thierry , Duerinck Johnny 
Published: August 2021

OBJECTIVE
The traditional freehand technique for external ventricular drain (EVD) placement is most frequently used, but remains the primary risk factor for inaccurate drain placement. As this procedure could benefit from image guidance, the authors set forth to demonstrate the impact of augmented-reality (AR) assistance on the accuracy and learning curve of EVD placement compared with the freehand technique.

METHODS
Sixteen medical students performed a total of 128 EVD placements on a custom-made phantom head, both before and after receiving a standardized training session. They were guided by either the freehand technique or by AR, which provided an anatomical overlay and tailored guidance for EVD placement through inside-out infrared tracking. The outcome was quantified by the metric accuracy of EVD placement as well as by its clinical quality.

RESULTS
The mean target error was significantly impacted by either AR (p = 0.003) or training (p = 0.02) in a direct comparison with the untrained freehand performance. Both untrained (11.9 ± 4.5 mm) and trained (12.2 ± 4.7 mm) AR performances were significantly better than the untrained freehand performance (19.9 ± 4.2 mm), which improved after training (13.5 ± 4.7 mm). The quality of EVD placement as assessed by the modified Kakarla scale (mKS) was significantly impacted by AR guidance (p = 0.005) but not by training (p = 0.07). Both untrained and trained AR performances (59.4% mKS grade 1 for both) were significantly better than the untrained freehand performance (25.0% mKS grade 1). Spatial aptitude testing revealed a correlation between perceptual ability and untrained AR-guided performance (r = 0.63).

CONCLUSIONS
Compared with the freehand technique, AR guidance for EVD placement yielded a higher outcome accuracy and quality for procedure novices. With AR, untrained individuals performed as well as trained individuals, which indicates that AR guidance not only improved performance but also positively impacted the learning curve. Future efforts will focus on the translation and evaluation of AR for EVD placement in the clinical setting.
Simulation of surgery for supratentorial gliomas in virtual reality using a 3D volume rendering technique: a poor man's neuronavigation
Authors: Gosal Jaskaran Singh , Tiwari Sarbesh , Sharma Tarunesh , Agrawal Mohit , Garg Mayank , Mahal Sayani , Bhaskar Suryanarayanan , Sharma Raghavendra Kumar , Janu Vikas , Jha Deepak Kumar 
Published: August 2021

OBJECTIVE
Different techniques of performing image-guided neurosurgery exist, namely, neuronavigation systems, intraoperative ultrasound, and intraoperative MRI, each with its limitations. Except for ultrasound, other methods are expensive. Three-dimensional virtual reconstruction and surgical simulation using 3D volume rendering (VR) is an economical and excellent technique for preoperative surgical planning and image-guided neurosurgery. In this article, the authors discuss several nuances of the 3D VR technique that have not yet been described.

METHODS
The authors included 6 patients with supratentorial gliomas who underwent surgery between January 2019 and March 2021. Preoperative clinical data, including patient demographics, preoperative planning details (done using the VR technique), and intraoperative details, including relevant photos and videos, were collected. RadiAnt software was used for generating virtual 3D images using the VR technique on a computer running Microsoft Windows.

RESULTS
The 3D VR technique assists in glioma surgery with a preoperative simulation of the skin incision and craniotomy, virtual cortical surface marking and navigation for deep-seated gliomas, preoperative visualization of morbid cortical surface and venous anatomy in surfacing gliomas, identifying the intervenous surgical corridor in both surfacing and deep-seated gliomas, and pre- and postoperative virtual 3D images highlighting the exact spatial geometric residual tumor location and extent of resection for low-grade gliomas (LGGs).

CONCLUSIONS
Image-guided neurosurgery with the 3D VR technique using RadiAnt software is an economical, easy-to-learn, and user-friendly method of simulating glioma surgery, especially in resource-constrained countries where expensive neuronavigation systems are not readily available. Apart from cortical sulci/gyri anatomy, FLAIR sequences are ideal for the 3D visualization of nonenhancing diffuse LGGs using the VR technique. In addition to cortical vessels (especially veins), contrast MRI sequences are perfect for the 3D visualization of contrast-enhancing high-grade gliomas.