From CNNs to diffusion models: a decade of advances in brain tumor and stroke lesion segmentation across BraTS, ATLAS, and ISLES benchmarks

Accurate segmentation of brain tumors and ischemic stroke lesions from magnetic resonance imaging (MRI) is a fundamental step in diagnosis, prognosis, and treatment planning. Over the past decade, segmentation research has advanced rapidly with the development of deep learning architectures, large annotated datasets, and diverse evaluation protocols. This survey provides a unified overview of segmentation techniques spanning both brain tumors and ischemic stroke lesions, offering a comprehensive perspective on their evolution, strengths, and limitations. We examine key benchmark datasets, including BraTS (2015–2023), ISLES (2015–2022), and ATLAS v2.0, and summarize their imaging modalities, annotations, and clinical contexts. Major model families such as U-Net variants, CNN-Transformer hybrids, transformer-only models, ensemble frameworks, and emerging diffusion-based approaches are systematically analysed with respect to design principles and reported performance across subregions and lesion types. The survey further compiles leaderboard results, state-of-the-art comparisons, and over 70 influential studies, highlighting region-wise trends and performance variability. Visual analyses, including histograms and box plots, offer additional insight into how segmentation accuracy differs across datasets and methods. We also review persistent challenges such as data scarcity, domain shift, boundary ambiguity, and clinical integration barriers, along with research trends including foundation models, semi- and self-supervised learning, and multimodal fusion. By consolidating datasets, methodologies, evaluation metrics, and future directions, this survey serves as a reference point for researchers and clinicians, outlining both the progress made and the opportunities that remain in developing robust and clinically relevant segmentation systems.