A Practical Guide to Manual and Semi-Automated Neurosurgical Brain Lesion Segmentation.

IF 1.6 Q3 CLINICAL NEUROLOGY NeuroSci Pub Date : 2024-08-02 eCollection Date: 2024-09-01 DOI:10.3390/neurosci5030021
Raunak Jain, Faith Lee, Nianhe Luo, Harpreet Hyare, Anand S Pandit
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Abstract

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.

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手动和半自动神经外科脑损伤分割实用指南》。
本文旨在为神经外科受训人员和研究人员提供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实用指南:所使用的医学影像来自医学影像计算和计算机辅助介入学会(MICCAI)的多模态脑肿瘤分割挑战赛(BRATS)图像数据库,以及当地图片存档和通信系统(PACS)的记录。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),因其可用性和强大的内置分割工具,当然也可使用其他工具(Seg3D、Freesurfer 和 3D Slicer)。质量控制是通过聘请专家分割师进行审查来实现的:结果:我们开发了一个流水线来演示病人图像的预处理、手动和半自动分割,以及每个颅骨病变的图像引导和视频记录。生成了三个分割样本,以说明潜在的挑战。在文本和视频中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。不过,在特定情况下,手动分割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。
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