ArticleJournal of digital imaging2020
State-of-the-Art Traditional to the Machine- and Deep-Learning-Based Skull Stripping Techniques, Models, and Algorithms.
Article in Journal of digital imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Cerebral ischemia detection using deep learning techniques.Health information science and systems · 2025Article
- Automated brain extraction for canine magnetic resonance images.BMC veterinary research · 2025Article
- Exploring dental faculty awareness, knowledge, and attitudes toward AI integration in education and practice: a mixed-method study.BMC medical education · 2025Article
- A lifespan-generalizable skull-stripping model for magnetic resonance images that leverages prior knowledge from brain atlases.Nature biomedical engineering · 2025Article
- Enhancing Radiologist Productivity with Artificial Intelligence in Magnetic Resonance Imaging (MRI): A Narrative Review.Diagnostics (Basel, Switzerland) · 2025Review
- Skull stripping tools in pediatric T2-weighted MRI scans: a retrospective evaluation of segmentation performance.Frontiers in neuroscience · 2025Article
- CSAMDT: Conditional Self Attention Memory-Driven Transformers for Radiology Report Generation from Chest X-Ray.Journal of imaging informatics in medicine · 2024Article
- Machine Learning-Driven GLCM Analysis of Structural MRI for Alzheimer's Disease Diagnosis.Bioengineering (Basel, Switzerland) · 2024Article
- Automated neonatal nnU-Net brain MRI extractor trained on a large multi-institutional dataset.Scientific reports · 2024Article
- Stroke Lesion Segmentation and Deep Learning: A Comprehensive Review.Bioengineering (Basel, Switzerland) · 2024Review
- A survey on brain tumor image analysis.Medical & biological engineering & computing · 2024Review
- Fully Automated Skull Stripping from Brain Magnetic Resonance Images Using Mask RCNN-Based Deep Learning Neural Networks.Brain sciences · 2023Article
- Recognizing Pediatric Tuberous Sclerosis Complex Based on Multi-Contrast MRI and Deep Weighted Fusion Network.Bioengineering (Basel, Switzerland) · 2023Article
- Validation of automatically measured T1 map cortico-medullary difference (ΔT1) for eGFR and fibrosis assessment in allograft kidneys.PloS one · 2023Article
- SynthStrip: skull-stripping for any brain image.NeuroImage · 2022Article
- GUBS: Graph-Based Unsupervised Brain Segmentation in MRI Images.Journal of imaging · 2022Article
- Where Is the Artificial Intelligence Applied in Dentistry? Systematic Review and Literature Analysis.Healthcare (Basel, Switzerland) · 2022Review
- Automated 2D Slice-Based Skull Stripping Multi-View Ensemble Model on NFBS and IBSR Datasets.Journal of digital imaging · 2022Article
- Use of Advanced Artificial Intelligence in Forensic Medicine, Forensic Anthropology and Clinical Anatomy.Healthcare (Basel, Switzerland) · 2021Article
- 3D U-Net Improves Automatic Brain Extraction for Isotropic Rat Brain Magnetic Resonance Imaging Data.Frontiers in neuroscience · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Several neuroimaging processing applications consider skull stripping as a crucial pre-processing step. Due to complex anatomical brain structure and intensity variations in brain magnetic resonance imaging (MRI), an appropriate skull stripping is an important part. The process of skull stripping basically deals with the removal of the skull region for clinical analysis in brain segmentation tasks, and its accuracy and efficiency are quite crucial for diagnostic purposes. It requires more accurate and detailed methods for differentiating brain regions and the skull regions and is considered as a challenging task. This paper is focused on the transition of the conventional to the machine- and deep-learning-based automated skull stripping methods for brain MRI images. It is observed in this study that deep learning approaches have outperformed conventional and machine learning techniques in many ways, but they have their limitations. It also includes the comparative analysis of the current state-of-the-art skull stripping methods, a critical discussion of some challenges, model of quantifying parameters, and future work directions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.