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How we won BraTS 2023 Adult Glioma challenge? Just faking it! Enhanced Synthetic Data Augmentation and Model Ensemble for brain tumour segmentation

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arxiv 2402.17317 v2 pith:JRXEH2M3 submitted 2024-02-27 eess.IV cs.CVcs.LG

André Ferreira , Naida Solak , Jianning Li , Philipp Dammann , Jens Kleesiek , Victor Alves , Jan Egger This is my paper
classification eess.IVcs.CVcs.LG
keywords tumourdatabrainchallengeaugmentationbratsdeepfirst
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Deep Learning is the state-of-the-art technology for segmenting brain tumours. However, this requires a lot of high-quality data, which is difficult to obtain, especially in the medical field. Therefore, our solutions address this problem by using unconventional mechanisms for data augmentation. Generative adversarial networks and registration are used to massively increase the amount of available samples for training three different deep learning models for brain tumour segmentation, the first task of the BraTS2023 challenge. The first model is the standard nnU-Net, the second is the Swin UNETR and the third is the winning solution of the BraTS 2021 Challenge. The entire pipeline is built on the nnU-Net implementation, except for the generation of the synthetic data. The use of convolutional algorithms and transformers is able to fill each other's knowledge gaps. Using the new metric, our best solution achieves the dice results 0.9005, 0.8673, 0.8509 and HD95 14.940, 14.467, 17.699 (whole tumour, tumour core and enhancing tumour) in the validation set.

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Cited by 5 Pith papers

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

  1. AI-Driven MRI-based Brain Tumour Segmentation Benchmarking

    cs.CV 2025-06 conditional novelty 5.0 of 10

    Fine-tuned SAM and SAM 2 with high-quality bounding-box prompts achieve higher Dice scores than zero-shot nnU-Net on pediatric brain tumor segmentation, but nnU-Net remains more practical.

  2. Enhancing Privacy: The Utility of Stand-Alone Synthetic CT and MRI for Tumor and Bone Segmentation

    eess.IV 2025-06 conditional novelty 5.0 of 10

    Synthetic MRI can replace real MRI for training brain tumor segmentation models, but synthetic CT cannot support head and neck tumor segmentation, though it works for simpler bone segmentation.

  3. Improving Pre-trained Adult Glioma Segmentation Models Using only Post-processing Techniques

    cs.CV 2025-12 unverdicted novelty 4.0 of 10

    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.

  4. GANet-Seg: Adversarial Learning for Brain Tumor Segmentation with Hybrid Generative Models

    eess.IV 2025-06 reject novelty 4.0 of 10

    GANet-Seg couples a pretrained normal-brain GAN with a U-Net for brain tumor segmentation, but its own Table 2 contradicts the abstract's claim of better HD95 than baselines.

  5. 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.

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