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<p>Accurate brain tumour segmentation on magnetic resonance imaging (MRI) is central to diagnosis, radiotherapy planning, radiomic analysis, and longitudinal monitoring. This PRISMA 2020-guided systematic review synthesises deep learning approaches for MRI-based brain tumour segmentation published between January 2018 and March 2025. After screening 2280 records and assessing 292 full texts, 111 studies were included. The review organises the literature into five methodological paradigms: convolutional neural network (CNN)/U-Net architectures, attention and hybrid encoder-decoder models, generative adversarial network (GAN)-based segmentation and augmentation, transformer and CNN-transformer models, and diffusion/probabilistic approaches. Beyond Dice performance, the synthesis examines validation design, benchmark dependence, uncertainty estimation, calibration, explainability, risk of bias, and clinical readiness. Most studies relied exclusively on BraTS datasets, while external validation, calibration analysis, and clinically interpretable uncertainty were uncommon. Transformer-based models generally reported the highest benchmark Dice scores, whereas diffusion and Bayesian approaches offered stronger uncertainty-aware modelling but remain less mature for deployment. The review concludes that future progress requires external multi-centre validation, transparent reporting, calibrated uncertainty, and prospective clinical workflow evaluation beyond benchmark optimisation.</p>

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Deep learning for brain tumour segmentation on MRI with focus on robustness uncertainty and clinical translation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月6日

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  • Behnam Kiani Kalejahi,
  • Mohammad Javad Rajabi

摘要

Accurate brain tumour segmentation on magnetic resonance imaging (MRI) is central to diagnosis, radiotherapy planning, radiomic analysis, and longitudinal monitoring. This PRISMA 2020-guided systematic review synthesises deep learning approaches for MRI-based brain tumour segmentation published between January 2018 and March 2025. After screening 2280 records and assessing 292 full texts, 111 studies were included. The review organises the literature into five methodological paradigms: convolutional neural network (CNN)/U-Net architectures, attention and hybrid encoder-decoder models, generative adversarial network (GAN)-based segmentation and augmentation, transformer and CNN-transformer models, and diffusion/probabilistic approaches. Beyond Dice performance, the synthesis examines validation design, benchmark dependence, uncertainty estimation, calibration, explainability, risk of bias, and clinical readiness. Most studies relied exclusively on BraTS datasets, while external validation, calibration analysis, and clinically interpretable uncertainty were uncommon. Transformer-based models generally reported the highest benchmark Dice scores, whereas diffusion and Bayesian approaches offered stronger uncertainty-aware modelling but remain less mature for deployment. The review concludes that future progress requires external multi-centre validation, transparent reporting, calibrated uncertainty, and prospective clinical workflow evaluation beyond benchmark optimisation.