ArticleJournal of magnetic resonance imaging : JMRI2020
Deep learning enables automatic detection and segmentation of brain metastases on multisequence MRI.
Article in Journal of magnetic resonance imaging : JMRI, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 116 papers, 1 of them a synthesis that pooled it.
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Who cites it
116 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Brain metastasis detection using machine learning: a systematic review and meta-analysis.Neuro-oncology · 2021Pooled it
- Automated segmentation of brain metastases with deep learning: A multi-center, randomized crossover, multi-reader evaluation study.Neuro-oncology · 2024Trial
- 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
- AI-Assisted Brain Tumor MRI Reporting and Treatment-Planning Segmentation: A Retrospective Paired Workflow Evaluation.Biomedicines · 2026Article
- A large-scale heterogeneous 3D magnetic resonance brain imaging dataset for self-supervised learning.Scientific data · 2026Article
- An advanced automated pipeline for brain tumour segmentation on magnetic resonance imaging for gamma knife radiosurgery.Physics and imaging in radiation oncology · 2026Article
- Report-Level Impact of DL Assistance on Teleradiology Quality Support for Brain Metastases: Real-World Clinical Practice at a Single Tertiary Center.Diagnostics (Basel, Switzerland) · 2026Article
- Breast Cancer Brain Metastases: Current Understanding and Future Directions.Current oncology reports · 2026Review
- Evaluation of deep learning-based methods for automatic detection and segmentation of brain metastases in T1-contrast MRI for stereotactic radiosurgery.Journal of applied clinical medical physics · 2026Article
- A large scale multi-dataset investigation of brain metastases distribution based on primary cancer type.BMC cancer · 2025Article
- Improving Vessel Visibility and Applying Artificial Intelligence to Autodetect Brain Metastasis for a 3D MR Imaging Sequence Capable of Simultaneous Images with and without Blood Vessel Suppression.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025Article
- Enhancing Brain Metastases Detection and Segmentation in Black-Blood MRI Using Deep Learning and Segment Anything Model (SAM).Yonsei medical journal · 2025Article
- Adaptive RAG-Assisted MRI Platform (ARAMP) for Brain Metastasis Detection and Reporting: A Retrospective Evaluation Using Post-Contrast T1-Weighted Imaging.Bioengineering (Basel, Switzerland) · 2025Article
- Article
- Semi-Supervised Learning Allows for Improved Segmentation With Reduced Annotations of Brain Metastases Using Multicenter MRI Data.Journal of magnetic resonance imaging : JMRI · 2025Article
- Comparative Study of Cell Nuclei Segmentation Based on Computational and Handcrafted Features Using Machine Learning Algorithms.Diagnostics (Basel, Switzerland) · 2025Article
- Automated segmentation of brain metastases in T1-weighted contrast-enhanced MR images pre and post stereotactic radiosurgery.BMC medical imaging · 2025Article
- Enhancing deep learning methods for brain metastasis detection through cross-technique annotations on SPACE MRI.European radiology experimental · 2025Article
- Artificial intelligence-assisted volume isotropic simultaneous interleaved bright- and black-blood examination for brain metastases.Neuroradiology · 2025Article
56 more citing papers are in PubMed but not listed here.
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Authors and funding
6 authors.
Funding
Abstract
backgroundDetecting and segmenting brain metastases is a tedious and time-consuming task for many radiologists, particularly with the growing use of multisequence 3D imaging. PURPOSE: To demonstrate automated detection and segmentation of brain metastases on multisequence MRI using a deep-learning approach based on a fully convolution neural network (CNN). STUDY TYPE: Retrospective. POPULATION: In all, 156 patients with brain metastases from several primary cancers were included. FIELD STRENGTH: 1.5T and 3T. [Correction added on May 24, 2019, after first online publication: In the preceding sentence, the first field strength listed was corrected.] SEQUENCE: Pretherapy MR images included pre- and postgadolinium T ASSESSMENT: The ground truth was established by manual delineation by two experienced neuroradiologists. CNN training/development was performed using 100 and 5 patients, respectively, with a 2.5D network based on a GoogLeNet architecture. The results were evaluated in 51 patients, equally separated into those with few (1-3), multiple (4-10), and many (>10) lesions. STATISTICAL TESTS: Network performance was evaluated using precision, recall, Dice/F1 score, and receiver operating characteristic (ROC) curve statistics. For an optimal probability threshold, detection and segmentation performance was assessed on a per-metastasis basis. The Wilcoxon rank sum test was used to test the differences between patient subgroups.
resultsThe area under the ROC curve (AUC), averaged across all patients, was 0.98 ± 0.04. The AUC in the subgroups was 0.99 ± 0.01, 0.97 ± 0.05, and 0.97 ± 0.03 for patients having 1-3, 4-10, and >10 metastases, respectively. Using an average optimal probability threshold determined by the development set, precision, recall, and Dice score were 0.79 ± 0.20, 0.53 ± 0.22, and 0.79 ± 0.12, respectively. At the same probability threshold, the network showed an average false-positive rate of 8.3/patient (no lesion-size limit) and 3.4/patient (10 mm DATA
conclusionA deep-learning approach using multisequence MRI can automatically detect and segment brain metastases with high accuracy. LEVEL OF EVIDENCE: 3 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;51:175-182.
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