NnU-Net versus mesh growing algorithm as a tool for the robust and timely segmentation of neurosurgical 3D images in contrast-enhanced T1 MRI scans.

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.

Evaluation Metrics for Augmented Reality in Neurosurgical Preoperative Planning, Surgical Navigation, and Surgical Treatment Guidance: A Systematic Review.

BACKGROUND AND OBJECTIVE: Recent years have shown an advancement in the development of augmented reality (AR) technologies for preoperative visualization, surgical navigation, and intraoperative guidance for neurosurgery. However, proving added value for AR in clinical practice is challenging, partly because of a lack of standardized evaluation metrics. We performed a systematic review to provide an overview of the reported evaluation metrics for AR technologies in neurosurgical practice and to establish a foundation for assessment and comparison of such technologies. METHODS: PubMed, Embase, and Cochrane were searched systematically for publications on assessment of AR for cranial neurosurgery on September 22, 2022. The findings were reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. RESULTS: The systematic search yielded 830 publications; 114 were screened full text, and 80 were included for analysis. Among the included studies, 5% dealt with preoperative visualization using AR, with user perception as the most frequently reported metric. The majority (75%) researched AR technology for surgical navigation, with registration accuracy, clinical outcome, and time measurements as the most frequently reported metrics. In addition, 20% studied the use of AR for intraoperative guidance, with registration accuracy, task outcome, and user perception as the most frequently reported metrics. CONCLUSION: For quality benchmarking of AR technologies in neurosurgery, evaluation metrics should be specific to the risk profile and clinical objectives of the technology. A key focus should be on using validated questionnaires to assess user perception; ensuring clear and unambiguous reporting of registration accuracy, precision, robustness, and system stability; and accurately measuring task performance in clinical studies. We provided an overview suggesting which evaluation metrics to use per AR application and innovation phase, aiming to improve the assessment of added value of AR for neurosurgical practice and to facilitate the integration in the clinical workflow.

Application of virtual and mixed reality for 3D visualization in intracranial aneurysm surgery planning: a systematic review.

Background: Precise preoperative anatomical visualization and understanding of an intracranial aneurysm (IA) are fundamental for surgical planning and increased intraoperative confidence. Application of virtual reality (VR) and mixed reality (MR), thus three-dimensional (3D) visualization of IAs could be significant in surgical planning. Authors provide an up-to-date overview of VR and MR applied to IA surgery, with specific focus on tailoring of the surgical treatment. Methods: A systematic analysis of the literature was performed in accordance with the PRISMA guidelines. Pubmed, and Embase were searched to identify studies reporting use of MR and VR 3D visualization in IA surgery during the last 25 years. Type and number of IAs, category of input scan, visualization techniques (screen, glasses or head set), inclusion of haptic feedback, tested population (residents, fellows, attending neurosurgeons), and aim of the study (surgical planning/rehearsal, neurosurgical training, methodological validation) were noted. Results: Twenty-eight studies were included. Eighteen studies (64.3%) applied VR, and 10 (35.7%) used MR. A positive impact on surgical planning was documented by 19 studies (67.9%): 17 studies (60.7%) chose the tailoring of the surgical approach as primary outcome of the analysis. A more precise anatomical visualization and understanding with VR and MR was endorsed by all included studies (100%). Conclusion: Application of VR and MR to perioperative 3D visualization of IAs allowed an improved understanding of the patient-specific anatomy and surgical preparation. This review describes a tendency to utilize mostly VR-platforms, with the primary goals of a more accurate anatomical understanding, surgical planning and rehearsal.