Multimodal Brain Tumor Segmentation Challenge 2019: Clinical Relevance
Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histological sub-regions, i.e. peritumoral edema, necrotic core, enhancing and non-enhancing tumor core. This intrinsic heterogeneity of gliomas is also portrayed in their imaging phenotype (appearance and shape), as their sub-regions are described by varying intensity profiles disseminated across multimodal MRI scans, reflecting varying tumor biological properties. Due to this highly heterogeneous appearance and shape, segmentation of brain tumors in multimodal MRI scans is one of the most challenging tasks in medical image analysis.
There is a growing body of literature on computational algorithms addressing this important task. Unfortunately, open datasets for designing and testing such algorithms are limited, and private data sets differ so widely that it is hard to compare the different segmentation strategies developed. Critical factors leading to these differences include, but not limited to, i) the imaging modalities employed, ii) the type of the tumor (GBM or LGG, primary or secondary tumors, solid or infiltratively growing), and iii) the state of disease (images may not only be acquired prior to treatment, but also post-operatively and therefore show radiotherapy effects and surgically-imposed cavities).
Towards this end, BraTS is making available a large dataset with accompanying delineations of the relevant tumor sub-regions.
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