The COVID-19 pandemic has disrupted neurosurgical training worldwide, with the shutdown of academic institutions and the reduction of elective surgical procedures. This impact has disproportionately affected LMICs (lower- and/or middle-income countries), already burdened by a lack of neurosurgical resources. Thus, a systematic review was conducted to examine these challenges and innovations developed to adapt effective teaching and learning for medical students and neurosurgical trainees. A systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols (PRISMA-P) and The Cochrane Handbook of Systematic Reviews of Interventions. MEDLINE, PubMed, Embase and Cochrane databases were accessed, searching and screening literature from December 2019 to 5th December 2020 with set inclusion and exclusion criteria. Screening identified 1254 articles of which 26 were included, providing data from 96 countries. Twenty-three studies reported transition to online learning, with 8 studies also mentioned redeployment into COVID wards with 2 studies mentioning missed surgical exposure as a consequence. Of 7 studies conducted in LMICs, 3 reported residents suffering financial insecurities from reduced surgical caseload and recession. Significant global disruption in neurosurgical teaching and training has arisen from the COVID-19 pandemic. Decreased surgical exposure has negatively impacted educational provision. However, advancements in virtual technology have allowed for more affordable, accessible training especially in LMICs. Using this, initiatives to reduce physical and mental stress experienced by trainees should be paramount.

The purpose of the article is to provide a practical guide for manual and semi-automated image segmentation of common neurosurgical cranial lesions, namely meningioma, glioblastoma multiforme (GBM) and subarachnoid haemorrhage (SAH), for neurosurgical trainees and researchers. Materials and Methods: The medical images used were sourced from the Medical Image Computing and Computer Assisted Interventions Society (MICCAI) Multimodal Brain Tumour Segmentation Challenge (BRATS) image database and from the local Picture Archival and Communication System (PACS) record with consent. Image pre-processing was carried out using MRIcron software (v1.0.20190902). ITK-SNAP (v3.8.0) was used in this guideline due to its availability and powerful built-in segmentation tools, although others (Seg3D, Freesurfer and 3D Slicer) are available. Quality control was achieved by employing expert segmenters to review. Results: A pipeline was developed to demonstrate the pre-processing and manual and semi-automated segmentation of patient images for each cranial lesion, accompanied by image guidance and video recordings. Three sample segmentations were generated to illustrate potential challenges. Advice and solutions were provided within both text and video. Conclusions: Semi-automated segmentation methods enhance efficiency, increase reproducibility, and are suitable to be incorporated into future clinical practise. However, manual segmentation remains a highly effective technique in specific circumstances and provides initial training sets for the development of more advanced semi- and fully automated segmentation algorithms.