ArticleNeuroradiology2020
Accuracy and practical aspects of semi- and fully automatic segmentation methods for resected brain areas.
Article in Neuroradiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic Value ofTechnology in cancer research & treatmentPooled it
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- Convolutional neural network based system for fully automatic FLAIR MRI segmentation in multiple sclerosis diagnosis.Scientific reports · 2025Article
- Morphologic and functional alterations in the parasagittal dural space in mild cognitive impairment.Scientific reports · 2025Article
- Transformers for Neuroimage Segmentation: Scoping Review.Journal of medical Internet research · 2025Article
- Automated generation of epilepsy surgery resection masks: The RAMPS pipeline.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- A Unified Pipeline for Simultaneous Brain Tumor Classification and Segmentation Using Fine-Tuned CNN and Residual UNet Architecture.Life (Basel, Switzerland) · 2024Article
- Comparative analysis of volumetric changes between resection volume of oral tongue cancer and post operative volume of radial forearm flaps.Clinical oral investigations · 2024Article
- Fully automatic segmentation of brain lacunas resulting from resective surgery using a 3D deep learning model.medRxiv : the preprint server for health sciences · 2024Article
- The Unresolved Methodological Challenge of Detecting Neuroplastic Changes in Astronauts.Life (Basel, Switzerland) · 2023Article
- Scalp HFO rates are higher for larger lesions.Epilepsia open · 2022Article
- Novel User-Friendly Application for MRI Segmentation of Brain Resection following Epilepsy Surgery.Diagnostics (Basel, Switzerland) · 2022Article
- ResectVol: A tool to automatically segment and characterize lacunas in brain images.Epilepsia open · 2021Article
- Predicting Breast Cancer Subtypes Using Magnetic Resonance Imaging Based Radiomics With Automatic Segmentation.Journal of computer assisted tomographyArticle
- Machine Learning for Precision Epilepsy Surgery.Epilepsy currentsReview
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7 authors.
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Abstract
purposePrecise segmentation of brain lesions is essential for neurological research. Specifically, resection volume estimates can aid in the assessment of residual postoperative tissue, e.g. following surgery for glioma. Furthermore, behavioral lesion-symptom mapping in epilepsy relies on accurate delineation of surgical lesions. We sought to determine whether semi- and fully automatic segmentation methods can be applied to resected brain areas and which approach provides the most accurate and cost-efficient results.
methodsWe compared a semi-automatic (ITK-SNAP) with a fully automatic (lesion_GNB) method for segmentation of resected brain areas in terms of accuracy with manual segmentation serving as reference. Additionally, we evaluated processing times of all three methods. We used T1w, MRI-data of epilepsy patients (n = 27; 11 m; mean age 39 years, range 16-69) who underwent temporal lobe resections (17 left).
resultsThe semi-automatic approach yielded superior accuracy (p < 0.001) with a median Dice similarity coefficient (mDSC) of 0.78 and a median average Hausdorff distance (maHD) of 0.44 compared with the fully automatic approach (mDSC 0.58, maHD 1.32). There was no significant difference between the median percent volume difference of the two approaches (p > 0.05). Manual segmentation required more human input (30.41 min/subject) and therefore inferring significantly higher costs than semi- (3.27 min/subject) or fully automatic approaches (labor and cost approaching zero).
conclusionSemi-automatic segmentation offers the most accurate results in resected brain areas with a moderate amount of human input, thus representing a viable alternative compared with manual segmentation, especially for studies with large patient cohorts.
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