Brain Tumor Segmentation

The BRATS2017 dataset. It contains 285 brain tumor MRI scans, with four MRI modalities as T1, T1ce, T2, and Flair for each scan. The dataset also provides full masks for brain tumors, with labels for ED, ET, NET/NCR. The segmentation evaluation is based on three tasks: WT, TC and ET segmentation. Source: Scribble-based Hierarchical Weakly Supervised Learning for Brain Tumor Segmentation Image Source: https://www.google.com/search?q=A+Modified+U-Net+Convolutional+Network+Featuring+a+Nearest-neighbor+Re-sampling-based+Elastic-Transformation+for+Brain+Tissue+Characterization+and+Segmentation&oq=A+Modified+U-Net+Convolutional+Network+Featuring+a+Nearest-neighbor+Re-sampling-based+Elastic-Transformation+for+Brain+Tissue+Characterization+and+Segmentation&aqs=chrome..69i57j69i64l3.296j0j4&sourceid=chrome&ie=UTF-8

The BraTS 2015 dataset is a dataset for brain tumor image segmentation. It consists of 220 high grade gliomas (HGG) and 54 low grade gliomas (LGG) MRIs. The four MRI modalities are T1, T1c, T2, and T2FLAIR. Segmented “ground truth” is provide about four intra-tumoral classes, viz. edema, enhancing tumor, non-enhancing tumor, and necrosis. Source: Brain MRI Tumor Segmentation with Adversarial Networks Image Source: https://sites.google.com/site/braintumorsegmentation/home/brats2015

BRATS 2013 is a brain tumor segmentation dataset consists of synthetic and real images, where each of them is further divided into high-grade gliomas (HG) and low-grade gliomas (LG). There are 25 patients with both synthetic HG and LG images and 20 patients with real HG and 10 patients with real LG images. For each patient, FLAIR, T1, T2, and post-Gadolinium T1 magnetic resonance (MR) image sequences are available. Source: Learning Fixed Points in Generative Adversarial Networks: From Image-to-Image Translation to Disease Detection and Localization Image Source: https://arxiv.org/pdf/1708.00377.pdf

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BRATS 2014 is a brain tumor segmentation dataset. Source: Learning Fixed Points in Generative Adversarial Networks: From Image-to-Image Translation to Disease Detection and Localization Image Source: http://people.csail.mit.edu/menze/papers/proceedings_miccai_brats_2014.pdf

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BRISC is a high-quality, expert-annotated MRI dataset curated for brain tumor segmentation and classification. It addresses common limitations in existing datasets (e.g., BraTS, Figshare), including class imbalance, narrow tumor focus, and annotation inconsistencies.