Jain Raunak, Lee Faith, Luo Nianhe, Hyare Harpreet, Pandit Anand S
UCL Medical School, University College London, London WC1E 6DE, UK;
Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, London WC1N 3BG, UK;
NeuroSci. 2024 Aug 2;5(3):265-275. doi: 10.3390/neurosci5030021. eCollection 2024 Sep.
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.
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.
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.
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.
本文旨在为神经外科实习生和研究人员提供一份实用指南，用于常见神经外科颅脑病变（即脑膜瘤、多形性胶质母细胞瘤（GBM）和蛛网膜下腔出血（SAH））的手动和半自动图像分割。
所使用的医学图像来自医学图像计算与计算机辅助干预协会（MICCAI）多模态脑肿瘤分割挑战赛（BRATS）图像数据库，并经同意从本地图像存档与通信系统（PACS）记录中获取。使用MRIcron软件（v1.0.20190902）进行图像预处理。本指南使用ITK-SNAP（v3.8.0），因为它可用且具有强大的内置分割工具，不过也有其他工具（Seg3D、Freesurfer和3D Slicer）可供使用。通过聘请专家分割人员进行审核来实现质量控制。
开发了一个流程，用于展示针对每种颅脑病变的患者图像的预处理以及手动和半自动分割，并配有图像指导和视频记录。生成了三个样本分割以说明潜在挑战。在文本和视频中都提供了建议和解决方案。
半自动分割方法提高了效率，增加了可重复性，适合纳入未来的临床实践。然而，手动分割在特定情况下仍然是一种非常有效的技术，并为开发更先进的半自动和全自动分割算法提供了初始训练集。
