I recently built a brain MRI segmentation project, that segments out tumors from MRI scans with 93% accuracy. In this article, however, I will be diving deeper into the open-source dataset that I used. This dataset was talked about in a research paper that I discuss in this article and has been linked to the bottom of the page. The Kaggle contributor for this particular dataset is Mateusz Buda, who is a Senior Machine Learning Engineer at IQVIA. The dataset was obtained from The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA).

Quantitative analysis of brain MRI is routine for many neurological diseases and conditions and relies on accurate segmentation of structures of interest. Deep learning-based segmentation approaches for brain MRI are gaining interest due to their self-learning and generalization ability over large amounts of data. As the deep learning architectures are becoming more mature, they gradually outperform previous state-of-the-art classical machine learning algorithms. This review aims to provide an overview of current deep learning-based segmentation approaches for quantitative brain MRI. First we review the current deep learning architectures used for segmentation of anatomical brain structures and brain lesions.