ArticleRadiology. Artificial intelligence2021
Three-dimensional U-Net Convolutional Neural Network for Detection and Segmentation of Intracranial Metastases.
Article in Radiology. Artificial intelligence, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed, 57 citations in OpenAlex.
- MRI-based two-stage deep learning model for automatic detection and segmentation of brain metastases.European radiology · 2023Trial
- Effect of Anatomic Contextual Information on the Performance of a Convolutional Neural Network Tasked With Brain Metastasis Detection.Advances in radiation oncology · 2026Article
- Opportunities and Challenges of Visual Large Language Models in Imaging Diagnostics: Lessons from Brain Metastasis Detection in Clinical MRI.Diagnostics (Basel, Switzerland) · 2026Article
- Semi-Supervised Learning Allows for Improved Segmentation With Reduced Annotations of Brain Metastases Using Multicenter MRI Data.Journal of magnetic resonance imaging : JMRI · 2025Article
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- Efficient slice anomaly detection network for 3D brain MRI Volume.PLOS digital health · 2025Article
- Automated segmentation of brain metastases in T1-weighted contrast-enhanced MR images pre and post stereotactic radiosurgery.BMC medical imaging · 2025Article
- MWG-UNet++: Hybrid Transformer U-Net Model for Brain Tumor Segmentation in MRI Scans.Bioengineering (Basel, Switzerland) · 2025Article
- A review of deep learning for brain tumor analysis in MRI.NPJ precision oncology · 2025Review
- Extended nnU-Net for Brain Metastasis Detection and Segmentation in Contrast-Enhanced Magnetic Resonance Imaging With a Large Multi-Institutional Data Set.International journal of radiation oncology, biology, physics · 2025Article
- Artificial intelligence in the task of segmentation and classification of brain metastases images: current challenges and future opportunities.Frontiers in neurology · 2025Review
- Automatic detection and multi-component segmentation of brain metastases in longitudinal MRI.Scientific reports · 2024Article
- Article
- Where Does Auto-Segmentation for Brain Metastases Radiosurgery Stand Today?Bioengineering (Basel, Switzerland) · 2024Review
- Incidence and imaging characteristics of difficult to detect retrospectively identified brain metastases in patients receiving repeat courses of stereotactic radiosurgery.Journal of neuro-oncology · 2024Article
- The University of California San Francisco Brain Metastases Stereotactic Radiosurgery (UCSF-BMSR) MRI Dataset.Radiology. Artificial intelligence · 2024Article
- Artificial Intelligence Detection and Segmentation Models: A Systematic Review and Meta-Analysis of Brain Tumors in Magnetic Resonance Imaging.Mayo Clinic proceedings. Digital health · 2024Article
- Computational Modeling and AI in Radiation Neuro-Oncology and Radiosurgery.Advances in experimental medicine and biology · 2024Review
- Improved robustness for deep learning-based segmentation of multi-center myocardial perfusion cardiovascular MRI datasets using data-adaptive uncertainty-guided space-time analysis.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2024Article
- Segmentation of Brain Metastases Using Background Layer Statistics (BLAST).AJNR. American journal of neuroradiology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo develop and validate a neural network for automated detection and segmentation of intracranial metastases on brain MRI studies obtained for stereotactic radiosurgery treatment planning. MATERIALS AND
methodsIn this retrospective study, 413 patients (average age, 61 years ± 12 [standard deviation]; 238 women) with a total of 5202 intracranial metastases (median volume, 0.05 cm
resultsThe median Dice score for segmenting enhancing metastases in the test set was 0.75 (interquartile range, 0.63-0.84). There were strong correlations between manually segmented and predicted metastasis volumes (
conclusionA 3D U-Net-based convolutional neural network was able to segment brain metastases with high accuracy and perform detection at the level of human interrater reliability for metastases larger than 6 mm.
Identifiers
What OpenQuestion holds
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.