ArticlePloS one2017
A deep convolutional neural network-based automatic delineation strategy for multiple brain metastases stereotactic radiosurgery.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 74 papers, 2 of them syntheses that pooled it.
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Who cites it
74 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Performance of artificial intelligence for brain tumor segmentation, treatment planning, and outcome prediction: a comprehensive systematic review and meta-analysis.Journal of neuro-oncology · 2025Pooled it
- Deep learning in MRI-guided radiation therapy: A systematic review.Journal of applied clinical medical physics · 2024Pooled it
- Saliency-guided deep learning network for automatic tumor bed volume delineation in post-operative breast irradiation.Physics in medicine and biology · 2021Trial
- Transformer-encoded nnU-Net with local region perceptron and contrastive learning (TLC-nnUNet) for multiple brain metastasis detection and delineation.Physics in medicine and biology · 2026Article
- A longitudinal MRI dataset of brain metastases with tumor segmentations, clinical & radiomic data.Scientific data · 2025Article
- AI-Driven Innovations in Neuroradiology and Neurosurgery: Scoping Review of Current Evidence and Future Directions.Cancers · 2025Review
- Enhancing Brain Metastases Detection and Segmentation in Black-Blood MRI Using Deep Learning and Segment Anything Model (SAM).Yonsei medical journal · 2025Article
- Semi-Supervised Learning Allows for Improved Segmentation With Reduced Annotations of Brain Metastases Using Multicenter MRI Data.Journal of magnetic resonance imaging : JMRI · 2025Article
- Embracing the Future of Clinical Trials in Radiation Therapy: An NRG Oncology CIRO Technology Retreat Whitepaper on Pioneering Technologies and AI-Driven Solutions.International journal of radiation oncology, biology, physics · 2025Review
- Enhancing deep learning methods for brain metastasis detection through cross-technique annotations on SPACE MRI.European radiology experimental · 2025Article
- 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
- Where Does Auto-Segmentation for Brain Metastases Radiosurgery Stand Today?Bioengineering (Basel, Switzerland) · 2024Review
- Deep learning for automated segmentation in radiotherapy: a narrative review.The British journal of radiology · 2024Review
- Raidionics: an open software for pre- and postoperative central nervous system tumor segmentation and standardized reporting.Scientific reports · 2023Article
- Self-configuring nnU-Net for automatic delineation of the organs at risk and target in high-dose rate cervical brachytherapy, a low/middle-income country's experience.Journal of applied clinical medical physics · 2023Article
- Automatically tracking brain metastases after stereotactic radiosurgery.Physics and imaging in radiation oncology · 2023Article
- A comprehensive dataset of annotated brain metastasis MR images with clinical and radiomic data.Scientific data · 2023Article
- Stratified assessment of an FDA-cleared deep learning algorithm for automated detection and contouring of metastatic brain tumors in stereotactic radiosurgery.Radiation oncology (London, England) · 2023Article
- [Robotics and computer-assisted procedures in cranial neurosurgery].Chirurgie (Heidelberg, Germany) · 2023Review
14 more citing papers are in PubMed but not listed here.
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Authors and funding
12 authors.
Funding
Abstract
Accurate and automatic brain metastases target delineation is a key step for efficient and effective stereotactic radiosurgery (SRS) treatment planning. In this work, we developed a deep learning convolutional neural network (CNN) algorithm for segmenting brain metastases on contrast-enhanced T1-weighted magnetic resonance imaging (MRI) datasets. We integrated the CNN-based algorithm into an automatic brain metastases segmentation workflow and validated on both Multimodal Brain Tumor Image Segmentation challenge (BRATS) data and clinical patients' data. Validation on BRATS data yielded average DICE coefficients (DCs) of 0.75±0.07 in the tumor core and 0.81±0.04 in the enhancing tumor, which outperformed most techniques in the 2015 BRATS challenge. Segmentation results of patient cases showed an average of DCs 0.67±0.03 and achieved an area under the receiver operating characteristic curve of 0.98±0.01. The developed automatic segmentation strategy surpasses current benchmark levels and offers a promising tool for SRS treatment planning for multiple brain metastases.
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