Abstract Introduction This study aimed to develop and validate a cost-effective, customizable patient-specific phantom for simulating external ventricular drain placement, combining image segmentation, 3-D printing and molding techniques. Methods Two variations of the phantom were created based on patient MRI data, integrating a realistic skin layer with anatomical landmarks, a 3-D printed skull, an agarose polysaccharide gel brain, and a ventricular cavity. To validate the phantom, 15 neurosurgeons, residents, and physician assistants performed 30 EVD placements. The effectiveness of the phantom as a training tool was assessed through a standardized user experience questionnaire, which evaluated the physical attributes, realism, and overall satisfaction. The mechanical properties of the phantom brain were quantified by measuring catheter insertion forces using a linear force tester to compare them to those experienced in real brain tissue. Results The study participants successfully completed EVD placements with a 76.7% optimal placement rate, which aligns with rates observed in clinical practice. Feedback highlighted the anatomical accuracy of the phantom and its value in enhancing surgical skills, though it also identified areas for improvement, particularly in the realism of the skin layer. Mechanical testing demonstrated that the insertion forces required were comparable to those encountered in actual brain tissue. Conclusions The developed phantom offers a realistic, low-cost, and adaptable model for EVD simulation. This tool is particularly beneficial for both training and research, with future enhancements planned to improve the realism of the skin and incorporate more anatomical features to increase the fidelity of the simulation.
A fetal scalp electrode (FSE) is a frequently used investigation during labor. However, it is an invasive procedure which can lead to complications. Our patient developed a very large brain abscess after initial superficial infection of the skin site due to an FSE. The patient was admitted to the hospital after an asymmetric growth of the skull was noticed with no further signs of clinical illness. MRI showed a very large brain abscess which was aspirated and treated with antibiotics for 10 weeks. A 2-year follow-up showed only a slight developmental delay in gross motor skills. Only once before a similar case has been described at which the patient developed a brain abscess after superficial infection of the scalp following an FSE. In both cases, the brain abscess was noticed due to an asymmetric growth of the skull without any further signs of clinical illness. A brain abscess has a high mortality and morbidity rate, and early diagnosis is vital for the optimal outcome. We therefore recommend to organize an out-patient clinical follow-up for every infant with a superficial infection of the skin site after placement of an FSE.
Holographic neuronavigation has several potential advantages compared to conventional neuronavigation systems. We present the first report of a holographic neuronavigation system with patient-to-image registration and patient tracking with a reference array using an augmented reality head-mounted display (AR-HMD).Three patients undergoing an intracranial neurosurgical procedure were included in this pilot study. The relevant anatomy was first segmented in 3D and then uploaded as holographic scene in our custom neuronavigation software. Registration was performed using point-based matching using anatomical landmarks. We measured the fiducial registration error (FRE) as the outcome measure for registration accuracy. A custom-made reference array with QR codes was integrated in the neurosurgical setup and used for patient tracking after bed movement.Six registrations were performed with a mean FRE of 8.5 mm. Patient tracking was achieved with no visual difference between the registration before and after movement.This first report shows a proof of principle of intraoperative patient tracking using a standalone holographic neuronavigation system. The navigation accuracy should be further optimized to be clinically applicable. However, it is likely that this technology will be incorporated in future neurosurgical workflows because the system improves spatial anatomical understanding for the surgeon.
Abstract Purpose This study evaluates the nnU-Net for segmenting brain, skin, tumors, and ventricles in contrast-enhanced T1 (T1CE) images, benchmarking it against an established mesh growing algorithm (MGA). Methods We used 67 retrospectively collected annotated single-center T1CE brain scans for training models for brain, skin, tumor, and ventricle segmentation. An additional 32 scans from two centers were used test performance compared to that of the MGA. The performance was measured using the Dice-Sørensen coefficient (DSC), intersection over union (IoU), 95th percentile Hausdorff distance (HD95), and average symmetric surface distance (ASSD) metrics, with time to segment also compared. Results The nnU-Net models significantly outperformed the MGA ( p < 0.0125) with a median brain segmentation DSC of 0.971 [95CI: 0.945–0.979], skin: 0.997 [95CI: 0.984–0.999], tumor: 0.926 [95CI: 0.508–0.968], and ventricles: 0.910 [95CI: 0.812–0.968]. Compared to the MGA’s median DSC for brain: 0.936 [95CI: 0.890, 0.958], skin: 0.991 [95CI: 0.964, 0.996], tumor: 0.723 [95CI: 0.000–0.926], and ventricles: 0.856 [95CI: 0.216–0.916]. NnU-Net performance between centers did not significantly differ except for the skin segmentations Additionally, the nnU-Net models were faster (mean: 1139 s [95CI: 685.0–1616]) than the MGA (mean: 2851 s [95CI: 1482–6246]). Conclusions The nnU-Net is a fast, reliable tool for creating automatic deep learning-based segmentation pipelines, reducing the need for extensive manual tuning and iteration. The models are able to achieve this performance despite a modestly sized training set. The ability to create high-quality segmentations in a short timespan can prove invaluable in neurosurgical settings.
Leptomeningeal metastases (LM) are a rare, but often debilitating complication of advanced cancer that can severely impact a patient's quality-of-life. LM can result in hydrocephalus (HC) and lead to a range of neurologic sequelae, including weakness, headaches, and altered mental status. Given that patients with LM generally have quite poor prognoses, the decision of how to manage this HC remains unclear and is not only a medical, but also an ethical one. We first provide a brief overview of management options for hydrocephalus secondary to LM. We then apply general ethical principles to decision making in LM-associated hydrocephalus that can help guide physicians and patients. Management options for LM-associated hydrocephalus include shunt placement, repeated lumbar punctures, intraventricular reservoir placement, endoscopic third ventriculostomy, or pain management alone without intervention. While these options may offer symptomatic relief in the short-term, each is also associated with risks to the patient. Moreover, data on survival and quality-of-life following intervention is sparse. We propose that the pros and cons of each option should be evaluated not only from a clinical standpoint, but also within a larger framework that incorporates ethical principles and individual patient values. The decision of how to manage LM-associated hydrocephalus is complex and requires close collaboration amongst the physician, patient, and/or patient's family/friends/community leaders. Ultimately, the decision should be rooted in the patients' values and should aim to optimize a patient's quality-of-life.
Background: Multiple 3D visualization techniques are available that obviates the need for the surgeon to mentally transform the 2D planes from MRI to the 3D anatomy of the patient. We assessed the spatial understanding of a brain tumour when visualized with MRI, 3D models on a monitor or 3D models in mixed reality. Methods: Medical students, neurosurgical residents and neurosurgeons were divided into three groups based on the imaging modality used for preparation: MRI, 3D viewer and mixed reality. After preparation, the participants needed to position, scale, and rotate a virtual tumour inside a virtual head of the patient in the same orientation as the original tumour would be. Primary outcome was the amount of overlap between the placed tumour and the original tumour to evaluate accuracy. Secondary outcomes were the position, volume and rotation deviation compared to the original tumour. Results: A total of 12 medical students, 12 neurosurgical residents, and 12 neurosurgeons were included. For medical students, the mean amount of overlap for the MRI, 3D viewer and mixed reality group was 0.26 (0.22), 0.38 (0.20) and 0.48 (0.20) respectively. For residents 0.45 (0.23), 0.45 (0.19) and 0.68 (0.11) and for neurosurgeons 0.39 (0.20), 0.50 (0.27) and 0.67 (0.14). The amount of overlap for mixed reality was significantly higher on all expertise levels compared to MRI and on resident and neurosurgeon level also compared to the 3D viewer. Furthermore, mixed reality showed the lowest deviations in position, volume and rotation on all expertise levels. Conclusion: Mixed reality enhances the spatial understanding of brain tumours compared to MRI and 3D models on a monitor. The preoperative use of mixed reality may therefore support the surgeon to improve spatial 3D related surgical tasks such as patient positioning and planning surgical trajectories.
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 cm 3 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 (HD 95 ). 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 HD 95 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 HD 95 (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 HD 95 ). 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 cm 3 . 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.
