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2019 | OriginalPaper | Chapter

Multi-task Learning for Neonatal Brain Segmentation Using 3D Dense-Unet with Dense Attention Guided by Geodesic Distance

Authors : Toan Duc Bui, Li Wang, Jian Chen, Weili Lin, Gang Li, Dinggang Shen

Published in: Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data

Publisher: Springer International Publishing

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Abstract

The deep convolutional neural network has achieved outstanding performance on neonatal brain MRI tissue segmentation. However, it may fail to produce reasonable results on unseen datasets that have different imaging appearance distributions with the training data. The main reason is that deep learning models tend to have a good fitting to the training dataset, but do not lead to a good generalization on the unseen datasets. To address this problem, we propose a multi-task learning method, which simultaneously learns both tissue segmentation and geodesic distance regression to regularize a shared encoder network. Furthermore, a dense attention gate is explored to force the network to learn rich contextual information. By using three neonatal brain datasets with different imaging protocols from different scanners, our experimental results demonstrate superior performance of our proposed method over the existing deep learning-based methods on the unseen datasets.
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Metadata
Title
Multi-task Learning for Neonatal Brain Segmentation Using 3D Dense-Unet with Dense Attention Guided by Geodesic Distance
Authors
Toan Duc Bui
Li Wang
Jian Chen
Weili Lin
Gang Li
Dinggang Shen
Copyright Year
2019
DOI
https://doi.org/10.1007/978-3-030-33391-1_28

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