Split-Attention U-Net: A Fully Convolutional Network for Robust Multi-Label Segmentation from Brain MRI

Split-Attention U-Net: A Fully Convolutional Network for Robust Multi-Label Segmentation from Brain MRI

Multi-label brain segmentation from brain magnetic resonance imaging (MRI) provides valuable structural information for most neurological analyses. Due to the complexity of the brain segmentation algorithm, it could delay the delivery of neuroimaging findings. Therefore, we introduce Split-Attention...

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Bibliographic Details
Main Authors: Minho Lee, JeeYoung Kim, Regina EY Kim, Hyun Gi Kim, Se Won Oh, Min Kyoung Lee, Sheng-Min Wang, Nak-Young Kim, Dong Woo Kang, ZunHyan Rieu, Jung Hyun Yong, Donghyeon Kim, Hyun Kook Lim
Format: Article
Language:English
Published: MDPI AG 2020-12-01
Series:Brain Sciences
Subjects:
multi-label brain segmentation
split-attention block
deep learning
fine-tuning
SAU-Net
Online Access:https://www.mdpi.com/2076-3425/10/12/974

Internet

https://www.mdpi.com/2076-3425/10/12/974

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