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The ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2023: Intracranial Meningioma

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arxiv 2305.07642 v1 pith:4PE7NJSB submitted 2023-05-12 cs.CV cs.AIcs.LGstat.ML

classification cs.CVcs.AIcs.LGstat.ML
keywords meningiomawillautomatedchallengemodelssegmentationtumorbrats
verification ladder T0 review T1 audit T2 compute T3 formal
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Meningiomas are the most common primary intracranial tumor in adults and can be associated with significant morbidity and mortality. Radiologists, neurosurgeons, neuro-oncologists, and radiation oncologists rely on multiparametric MRI (mpMRI) for diagnosis, treatment planning, and longitudinal treatment monitoring; yet automated, objective, and quantitative tools for non-invasive assessment of meningiomas on mpMRI are lacking. The BraTS meningioma 2023 challenge will provide a community standard and benchmark for state-of-the-art automated intracranial meningioma segmentation models based on the largest expert annotated multilabel meningioma mpMRI dataset to date. Challenge competitors will develop automated segmentation models to predict three distinct meningioma sub-regions on MRI including enhancing tumor, non-enhancing tumor core, and surrounding nonenhancing T2/FLAIR hyperintensity. Models will be evaluated on separate validation and held-out test datasets using standardized metrics utilized across the BraTS 2023 series of challenges including the Dice similarity coefficient and Hausdorff distance. The models developed during the course of this challenge will aid in incorporation of automated meningioma MRI segmentation into clinical practice, which will ultimately improve care of patients with meningioma.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. RadiomicsRetrieval: A Customizable Framework for Medical Image Retrieval Using Radiomics Features

    cs.CV 2025-07 conditional novelty 6.0 of 10

    A 3D medical image retrieval framework that aligns tumor image embeddings with radiomics features via contrastive learning, enabling queries by image, anatomical location, or partial feature sets.

  2. Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective

    eess.IV 2025-06 conditional novelty 5.0 of 10

    A dual-branch frequency-domain brain tumor segmentation network, HFF-Net, reports large Dice improvements over prior methods on four public MRI datasets, especially for contrast-enhancing tumor regions.

  3. BraTS orchestrator : Democratizing and Disseminating state-of-the-art brain tumor image analysis

    eess.IV 2025-06 conditional novelty 4.0 of 10

    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.

  4. BrainLesion Suite: A Flexible and User-Friendly Framework for Modular Brain Lesion Image Analysis

    cs.CV 2025-07 conditional novelty 3.0 of 10

    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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