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

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:...

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Veröffentlicht in:NeuroSci Jg. 5; H. 3; S. 265 - 275
Hauptverfasser: Jain, Raunak, Lee, Faith, Luo, Nianhe, Hyare, Harpreet, Pandit, Anand S.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland MDPI 01.09.2024
MDPI AG
Schlagworte:
education
glioblastoma
meningioma
radiomics
segmentation
Technical Note
meningioma
radiomics
segmentation
education
glioblastoma
ISSN:2673-4087, 2673-4087
Online-Zugang:Volltext
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Zusammenfassung: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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ISSN:2673-4087
2673-4087
DOI:10.3390/neurosci5030021