In this paper, we exploit a cascaded U-Net architecture to perform detection and segmentation of brain tumors (low- and high-grade gliomas) from magnetic resonance scans. First, we detect tumors in a binary-classification setting, and they later undergo multi-class segmentation. The total processing time of a single input volume amounts to around 15 s using a single GPU. The preliminary experiments over the BraTS’19 validation set revealed that our approach delivers high-quality tumor delineation and offers instant segmentation.
CITATION STYLE
Kotowski, K., Nalepa, J., & Dudzik, W. (2020). Detection and segmentation of brain tumors from MRI using U-Nets. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11993 LNCS, pp. 179–190). Springer. https://doi.org/10.1007/978-3-030-46643-5_17
Mendeley helps you to discover research relevant for your work.