ArticleFrontiers in neuroinformatics2022
2.5D and 3D segmentation of brain metastases with deep learning on multinational MRI data.
Article in Frontiers in neuroinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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19 citing papers in PubMed, 31 citations in OpenAlex.
- GDNet: A Robust 2.5D Multimodal MRI Brain Tumor Segmentation Framework with EMA Stabilization and Tumor-Aware Sampling.Journal of imaging · 2026Article
- A perioperative multi-modal fusion and deep learning-based prognostic system for upper tract urothelial carcinoma: a multi-institutional study.Insights into imaging · 2026Article
- Interpretable Multiomics Models for Predicting Surgical Interventions and Blood Transfusion Requirements in Traumatic Brain Injury.NPJ digital medicine · 2025Article
- 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
- Integrative habitat analysis and multi-instance deep learning for predictive model of PD-1/PD-L1 immunotherapy efficacy in NSCLC patients: a dual-center retrospective study.BMC medical imaging · 2025Article
- Preoperative prediction value of 2.5D deep learning model based on contrast-enhanced CT for lymphovascular invasion of gastric cancer.Scientific reports · 2025Article
- Detection and segmentation of brain metastases on MRI using 3D-MedDCNet.Medical physics · 2025Article
- 2.5D deep learning radiomics and clinical data for predicting occult lymph node metastasis in lung adenocarcinoma.BMC medical imaging · 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
- The value of predicting breast cancer with a DBT 2.5D deep learning model.Discover oncology · 2025Article
- A review of deep learning for brain tumor analysis in MRI.NPJ precision oncology · 2025Review
- 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
- 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
- Whole-body PET image denoising for reduced acquisition time.Frontiers in medicine · 2024Article
- Comparison of 2D, 2.5D, and 3D segmentation networks for maxillary sinuses and lesions in CBCT images.BMC oral health · 2023Article
- Development of RLK-Unet: a clinically favorable deep learning algorithm for brain metastasis detection and treatment response assessment.Frontiers in oncology · 2023Article
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Authors and funding
13 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Management of patients with brain metastases is often based on manual lesion detection and segmentation by an expert reader. This is a time- and labor-intensive process, and to that end, this work proposes an end-to-end deep learning segmentation network for a varying number of available MRI available sequences. Methods: We adapt and evaluate a 2.5D and a 3D convolution neural network trained and tested on a retrospective multinational study from two independent centers, in addition, nnU-Net was adapted as a comparative benchmark. Segmentation and detection performance was evaluated by: (1) the dice similarity coefficient, (2) a per-metastases and the average detection sensitivity, and (3) the number of false positives. Results: The 2.5D and 3D models achieved similar results, albeit the 2.5D model had better detection rate, whereas the 3D model had fewer false positive predictions, and nnU-Net had fewest false positives, but with the lowest detection rate. On MRI data from center 1, the 2.5D, 3D, and nnU-Net detected 79%, 71%, and 65% of all metastases; had an average per patient sensitivity of 0.88, 0.84, and 0.76; and had on average 6.2, 3.2, and 1.7 false positive predictions per patient, respectively. For center 2, the 2.5D, 3D, and nnU-Net detected 88%, 86%, and 78% of all metastases; had an average per patient sensitivity of 0.92, 0.91, and 0.85; and had on average 1.0, 0.4, and 0.1 false positive predictions per patient, respectively. Discussion/Conclusion: Our results show that deep learning can yield highly accurate segmentations of brain metastases with few false positives in multinational data, but the accuracy degrades for metastases with an area smaller than 0.4 cm
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