ArticleEuropean radiology2021
Robust performance of deep learning for automatic detection and segmentation of brain metastases using three-dimensional black-blood and three-dimensional gradient echo imaging.
Article in European radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- Effect of Anatomic Contextual Information on the Performance of a Convolutional Neural Network Tasked With Brain Metastasis Detection.Advances in radiation oncology · 2026Article
- Deep learning-based image enhancement for improved black blood imaging in brain metastasis.European radiology · 2026Article
- Evaluating the efficacy of a federated AI algorithm in assisting in the detection and segmentation of brain metastases: limitations and opportunities.Journal of neuro-oncology · 2025Article
- Artificial intelligence in cancer: applications, challenges, and future perspectives.Molecular cancer · 2025Review
- 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
- 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
- Article
- 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
- Artificial intelligence in the task of segmentation and classification of brain metastases images: current challenges and future opportunities.Frontiers in neurology · 2025Review
- Development and validation of a deep learning model using MR imaging for predicting brain metastases: an accuracy-focused study.Frontiers in oncology · 2025Article
- Reducing false positives in deep learning-based brain metastasis detection by using both gradient-echo and spin-echo contrast-enhanced MRI: validation in a multi-center diagnostic cohort.European radiology · 2024Article
- Article
- Deep learning-based detection and quantification of brain metastases on black-blood imaging can provide treatment suggestions: a clinical cohort study.European radiology · 2024Article
- SSL-QALAS: Self-Supervised Learning for rapid multiparameter estimation in quantitative MRI using 3D-QALAS.Magnetic resonance in medicine · 2023Article
- Construction and evaluation of a gated high-resolution neural network for automatic brain metastasis detection and segmentation.European radiology · 2023Article
- Developing and Evaluating Deep Learning Algorithms for Object Detection: Key Points for Achieving Superior Model Performance.Korean journal of radiology · 2023Review
- Article
- Automatic detection of punctate white matter lesions in infants using deep learning of composite images from two cases.Scientific reports · 2023Article
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8 authors.
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Abstract
objectivesTo evaluate whether a deep learning (DL) model using both three-dimensional (3D) black-blood (BB) imaging and 3D gradient echo (GRE) imaging may improve the detection and segmentation performance of brain metastases compared to that using only 3D GRE imaging.
methodsA total of 188 patients with brain metastases (917 lesions) who underwent a brain metastasis MRI protocol including contrast-enhanced 3D BB and 3D GRE were included in the training set. DL models based on 3D U-net were constructed. The models were validated in the test set consisting of 45 patients with brain metastases (203 lesions) and 49 patients without brain metastases.
resultsThe combined 3D BB and 3D GRE model yielded better performance than the 3D GRE model (sensitivities of 93.1% vs 76.8%, p < 0.001), and this effect was significantly stronger in subgroups with small metastases (p interaction < 0.001). For metastases < 3 mm, ≥ 3 mm and < 10 mm, and ≥ 10 mm, the sensitivities were 82.4%, 93.2%, and 100%, respectively. The combined 3D BB and 3D GRE model showed a false-positive per case of 0.59 in the test set. The combined 3D BB and 3D GRE model showed a Dice coefficient of 0.822, while 3D GRE model showed a lower Dice coefficient of 0.756.
conclusionsThe combined 3D BB and 3D GRE DL model may improve the detection and segmentation performance of brain metastases, especially in detecting small metastases. KEY POINTS: • The combined 3D BB and 3D GRE model yielded better performance for the detection of brain metastases than the 3D GRE model (p < 0.001), with sensitivities of 93.1% and 76.8%, respectively. • The combined 3D BB and 3D GRE model showed a false-positive rate per case of 0.59 in the test set. • The combined 3D BB and 3D GRE model showed a Dice coefficient of 0.822, while the 3D GRE model showed a lower Dice coefficient of 0.756.
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