ArticleBrain tumor segmentation, and cross-modality domain adaptation for medical image segmentation : MICCAI challenges, BraTS 2023 and CrossMoDA 2023, held in conjunction with MICCAI 2023, Vancouver, BC, Canada, October 12 and 8, 2024 : proc...2024
Model Ensemble for Brain Tumor Segmentation in Magnetic Resonance Imaging.
Article in Brain tumor segmentation, and cross-modality domain adaptation for medical image segmentation : MICCAI challenges, BraTS 2023 and CrossMoDA 2023, held in conjunction with MICCAI 2023, Vancouver, BC, Canada, October 12 and 8, 2024 : proc..., 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Hierarchical two-stage ensemble of dilated U-net models for brain tumor segmentation.Visual computing for industry, biomedicine, and art · 2026Article
- Generative AI for spatial tumor growth on MRI: a proof-of-principle study in pediatric diffuse midline glioma.BMC medicine · 2026Article
- Distilling population specific expertise into a unified model for generalizable brain tumor segmentation.Scientific reports · 2026Article
- Adaptable Segmentation Pipeline for Diverse Brain Tumors with Radiomic-guided Subtyping and Lesion-Wise Model Ensemble.Segmentation, classification, and synthesis for brain tumors and traumatic brain injuries : MICCAI 2025 Challenges: BraTS-Lighthouse 2025 and AIMS-TBI 2025, held in conjunction with MICCAI 2025, Daejeon, South Korea, September 23, 2025,... · 2026Article
- Improving Pre-trained Adult Glioma Segmentation Models using only Post-processing Techniques.Segmentation, classification, and synthesis for brain tumors and traumatic brain injuries : MICCAI 2025 Challenges: BraTS-Lighthouse 2025 and AIMS-TBI 2025, held in conjunction with MICCAI 2025, Daejeon, South Korea, September 23, 2025,... · 2026Article
- PruDensNet: a parameter efficient depthwise separable CNN for MRI-based brain tumor classification.Frontiers in medicine · 2026Article
- A novel hybrid framework integrating GA-driven 3D ResUNetGAN for MRI brain tumor segmentation.PloS one · 2026Article
- Deep learning strategies for semantic segmentation of pediatric brain tumors in multiparametric MRI.Scientific reports · 2025Article
- Advancing Precision: A Comprehensive Review of MRI Segmentation Datasets from BraTS Challenges (2012-2025).Sensors (Basel, Switzerland) · 2025Review
- Early prognostication of overall survival for pediatric diffuse midline gliomas using MRI radiomics and machine learning: a two-center study.medRxiv : the preprint server for health sciences · 2024Article
- Early prognostication of overall survival for pediatric diffuse midline gliomas using MRI radiomics and machine learning: A two-center study.Neuro-oncology advancesArticle
- Brain tumor segmentation in Sub-Saharan Africa patient population: The BraTS-Africa challenge.Neuro-oncology advancesReview
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11 authors.
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Abstract
Segmenting brain tumors in multi-parametric magnetic resonance imaging enables performing quantitative analysis in support of clinical trials and personalized patient care. This analysis provides the potential to impact clinical decision-making processes, including diagnosis and prognosis. In 2023, the well-established Brain Tumor Segmentation (BraTS) challenge presented a substantial expansion with eight tasks and 4,500 brain tumor cases. In this paper, we present a deep learning-based ensemble strategy that is evaluated for newly included tumor cases in three tasks: pediatric brain tumors (PED), intracranial meningioma (MEN), and brain metastases (MET). In particular, we ensemble outputs from state-of-the-art nnU-Net and Swin UNETR models on a region-wise basis. Furthermore, we implemented a targeted post-processing strategy based on a cross-validated threshold search to improve the segmentation results for tumor sub-regions. The evaluation of our proposed method on unseen test cases for the three tasks resulted in lesion-wise Dice scores for PED: 0.653, 0.809, 0.826; MEN: 0.876, 0.867, 0.849; and MET: 0.555, 0.6, 0.58; for the enhancing tumor, tumor core, and whole tumor, respectively. Our method was ranked first for PED, third for MEN, and fourth for MET, respectively.
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