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The 2024 Brain Tumor Segmentation (BraTS) Challenge: Glioma Segmentation on Post-treatment MRI
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Gliomas are the most common malignant primary brain tumors in adults and one of the deadliest types of cancer. There are many challenges in treatment and monitoring due to the genetic diversity and high intrinsic heterogeneity in appearance, shape, histology, and treatment response. Treatments include surgery, radiation, and systemic therapies, with magnetic resonance imaging (MRI) playing a key role in treatment planning and post-treatment longitudinal assessment. The 2024 Brain Tumor Segmentation (BraTS) challenge on post-treatment glioma MRI will provide a community standard and benchmark for state-of-the-art automated segmentation models based on the largest expert-annotated post-treatment glioma MRI dataset. Challenge competitors will develop automated segmentation models to predict four distinct tumor sub-regions consisting of enhancing tissue (ET), surrounding non-enhancing T2/fluid-attenuated inversion recovery (FLAIR) hyperintensity (SNFH), non-enhancing tumor core (NETC), and resection cavity (RC). Models will be evaluated on separate validation and test datasets using standardized performance metrics utilized across the BraTS 2024 cluster of challenges, including lesion-wise Dice Similarity Coefficient and Hausdorff Distance. Models developed during this challenge will advance the field of automated MRI segmentation and contribute to their integration into clinical practice, ultimately enhancing patient care.
Forward citations
Cited by 11 Pith papers
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Patient-specific energy manifolds from baseline mpMRI scans act as fixed geometric references to monitor longitudinal evolution of voxel distributions in sequence space for neuro-oncology proof-of-concept cases.
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A Voxel-Wise Uncertainty-Guided Framework for Glioma Segmentation Using Spherical Projection-Based U-Net and Localized Refinement in Multi-Parametric MRI
An uncertainty-guided hybrid 2D/3D U-Net reports improved glioma segmentation Dice on BraTS2020, but the reported improvement is undermined because the fusion weights were fit directly on the test set.
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Uncovering Neuroimaging Biomarkers of Brain Tumor Surgery with AI-Driven Methods
On 49 glioma patients with pre- and post-surgery MRI, PCA plus explainable AI identifies brain regions tied to survival, but the predictive model is weak and the explanation evaluation is partly circular.
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Post-Operative Glioma Segmentation via Loss Stabilization, Normalization and Subspace Attention
Post-operative brain-tumor segmentation is shown to be more stable across hospitals with a hybrid Dice-cross-entropy loss than with Generalized Dice Loss, helped by percentile normalization and a subspace attention block.
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Improving Pre-trained Adult Glioma Segmentation Models Using only Post-processing Techniques
Radiomics-guided thresholds that delete small components and relabel swapped tissue classes improved the BraTS 2025 ranking metric by 14.9% (SSA) and 0.9% (GLI) with zero GPU hours.
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No Modality Left Behind: Adapting to Missing Modalities via Knowledge Distillation for Brain Tumor Segmentation
AdaMM uses graph-guided adapters, bi-bottleneck distillation, and lesion-presence priors to maintain brain tumor segmentation accuracy when MRI modalities are missing.
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Bridging the Gap in Missing Modalities: Leveraging Knowledge Distillation and Style Matching for Brain Tumor Segmentation
MST-KDNet improves missing-modality brain tumor segmentation by distilling attention, logits, and style features from a complete-modality teacher to a student.
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Pre- and Post-Treatment Glioma Segmentation with the Medical Imaging Segmentation Toolkit
On BraTS 2025 glioma segmentation, postprocessing strategies improve mean Dice/HD95 but worsen the official rank-based score, so the authors submitted the unpostprocessed baseline.
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BraTS orchestrator : Democratizing and Disseminating state-of-the-art brain tumor image analysis
BraTS orchestrator is a new open-source package that provides uniform, tutorial-based access to winning BraTS segmentation and synthesis algorithms for brain tumor MRI.
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BrainLesion Suite: A Flexible and User-Friendly Framework for Modular Brain Lesion Image Analysis
BrainLesion Suite is a modular open-source toolkit for brain lesion image analysis that combines previously published preprocessing, segmentation, and evaluation components, though the paper reports no new quantitativ...
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F3-Net: Foundation Model for Full Abnormality Segmentation of Medical Images with Flexible Input Modality Requirement
F3-Net combines multi-encoder nnU-Net with zero-filled missing modalities to segment glioma, metastasis, stroke, and white matter lesions, but the missing-modality claim is untested and comparisons are incomplete.
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