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Erschienen in:
End-to-End Learning of a Conditional Random Field for Intra-retinal Layer Segmentation in Optical Coherence Tomography Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks

verfasst von : Hao Dong, Guang Yang, Fangde Liu, Yuanhan Mo, Yike Guo

Erschienen in: Medical Image Understanding and Analysis

Verlag: Springer International Publishing

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Abstract

A major challenge in brain tumor treatment planning and quantitative evaluation is determination of the tumor extent. The noninvasive magnetic resonance imaging (MRI) technique has emerged as a front-line diagnostic tool for brain tumors without ionizing radiation. Manual segmentation of brain tumor extent from 3D MRI volumes is a very time-consuming task and the performance is highly relied on operator’s experience. In this context, a reliable fully automatic segmentation method for the brain tumor segmentation is necessary for an efficient measurement of the tumor extent. In this study, we propose a fully automatic method for brain tumor segmentation, which is developed using U-Net based deep convolutional networks. Our method was evaluated on Multimodal Brain Tumor Image Segmentation (BRATS 2015) datasets, which contain 220 high-grade brain tumor and 54 low-grade tumor cases. Cross-validation has shown that our method can obtain promising segmentation efficiently.

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Vorheriges Kapitel Context-Aware Convolutional Neural Networks for Stroke Sign Detection in Non-contrast CT Scans
Nächstes Kapitel Modeling Diffusion Directions of Corpus Callosum
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Metadaten
Titel
Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks
verfasst von
Hao Dong
Guang Yang
Fangde Liu
Yuanhan Mo
Yike Guo
Copyright-Jahr
2017
Buch
Medical Image Understanding and Analysis

Print ISBN: 978-3-319-60963-8

Electronic ISBN: 978-3-319-60964-5

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-60964-5_44

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