ArticleInternational journal of computer assisted radiology and surgery2020
Manual segmentation versus semi-automated segmentation for quantifying vestibular schwannoma volume on MRI.
Article in International journal of computer assisted radiology and surgery, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis that pooled it.
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Who cites it
34 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Volumetric segmentation in the context of posterior fossa-related pathologies: a systematic review.Neurosurgical review · 2024Pooled it
- Deep learning for accurate tumour volume measurement and prediction of therapy response in paediatric osteosarcoma.European radiology · 2026Article
- Comparison of Two Methods for Assessing the Maxillary Sinus Volume in Patients with and Without Unilateral Cleft Lip and Palate: A Retrospective Cross-Sectional Study.Diagnostics (Basel, Switzerland) · 2026Article
- Utility of Artificial Intelligence in Stereotactic Radiosurgery for Vestibular Schwannomas: A Systematic Review.Asian journal of neurosurgery · 2026Review
- Deep learning-based detection of murine congenital heart defects from µCT scans.Communications biology · 2025Article
- Radiomic Applications in Skull Base Pathology: A Systematic Review of Potential Clinical Uses.Journal of neurological surgery. Part B, Skull base · 2025Article
- Geometric Fidelity of Magnetic Resonance Imaging and Computed Tomography-Derived Virtual 3D Models of Porcine Cadaver Mandibles: Conventional Versus Artificial Intelligence-Based Segmentation.Oral health & preventive dentistry · 2025Article
- A ResNet-50-UNet Hybrid with Whale Optimization Algorithm for Accurate Liver Tumor Segmentation.Diagnostics (Basel, Switzerland) · 2025Article
- A deep learning model for accurate segmentation of the Drosophila melanogaster brain from Micro-CT imaging.Developmental biology · 2025Article
- FasNet: a hybrid deep learning model with attention mechanisms and uncertainty estimation for liver tumor segmentation on LiTS17.Scientific reports · 2025Article
- A Multidimensional Particle Swarm Optimization-Based Algorithm for Brain MRI Tumor Segmentation.Sensors (Basel, Switzerland) · 2025Article
- The Real-World Impact of Vestibular Schwannoma Fully Automated Volume Measures on the Evaluation of Size Change and Clinical Management Outcomes in a Multidisciplinary Meeting Setting.The journal of international advanced otology · 2025Article
- Anatomical significance of the vestibular aqueduct in posterior wall of the internal auditory canal drilling through the retrosigmoid approach: a study utilizing 3D reconstruction technology.Neurosurgical review · 2025Article
- Target-aware cross-modality unsupervised domain adaptation for vestibular schwannoma and cochlea segmentation.Scientific reports · 2024Article
- Article
- Defining tumor growth in vestibular schwannomas: a volumetric inter-observer variability study in contrast-enhanced T1-weighted MRI.Neuroradiology · 2024Article
- A Practical Guide to Manual and Semi-Automated Neurosurgical Brain Lesion Segmentation.NeuroSci · 2024Article
- Oncologic Applications of Artificial Intelligence and Deep Learning Methods in CT Spine Imaging-A Systematic Review.Cancers · 2024Review
- Evolution of the Transforaminal Lumbar Interbody Fusion (TLIF): From Open to Percutaneous to Patient-Specific.Journal of clinical medicine · 2024Review
- Estimation of the Ischemic Lesion in the Experimental Stroke Studies Using Magnetic Resonance Imaging (Review).Bulletin of experimental biology and medicine · 2024Review
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
purposeManagement of vestibular schwannoma (VS) is based on tumour size as observed on T1 MRI scans with contrast agent injection. The current clinical practice is to measure the diameter of the tumour in its largest dimension. It has been shown that volumetric measurement is more accurate and more reliable as a measure of VS size. The reference approach to achieve such volumetry is to manually segment the tumour, which is a time intensive task. We suggest that semi-automated segmentation may be a clinically applicable solution to this problem and that it could replace linear measurements as the clinical standard.
methodsUsing high-quality software available for academic purposes, we ran a comparative study of manual versus semi-automated segmentation of VS on MRI with 5 clinicians and scientists. We gathered both quantitative and qualitative data to compare the two approaches; including segmentation time, segmentation effort and segmentation accuracy.
resultsWe found that the selected semi-automated segmentation approach is significantly faster (167 s vs 479 s, [Formula: see text]), less temporally and physically demanding and has approximately equal performance when compared with manual segmentation, with some improvements in accuracy. There were some limitations, including algorithmic unpredictability and error, which produced more frustration and increased mental effort in comparison with manual segmentation.
conclusionWe suggest that semi-automated segmentation could be applied clinically for volumetric measurement of VS on MRI. In future, the generic software could be refined for use specifically for VS segmentation, thereby improving accuracy.
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Registered trials
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