Detection and Segmentation of Brain Tumors from MRI Using U-Nets

Autor:	Jakub Nalepa, Krzysztof Kotowski, Wojciech Dudzik
Rok vydání:	2020
Předmět:	medicine.diagnostic_test business.industry Computer science Deep learning Brain tumor Pattern recognition Magnetic resonance imaging medicine.disease Set (abstract data type) medicine Segmentation Artificial intelligence business Volume (compression)
Zdroj:	Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries ISBN: 9783030466428 BrainLes@MICCAI (2)
DOI:	10.1007/978-3-030-46643-5_17
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::ca0f4f707e9a865b34d146e516966523 https://doi.org/10.1007/978-3-030-46643-5_17 Zobrazit plný text záznamu