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2019 | OriginalPaper | Chapter

Transfer Learning from Partial Annotations for Whole Brain Segmentation

Authors : Chengliang Dai, Yuanhan Mo, Elsa Angelini, Yike Guo, Wenjia Bai

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

Brain MR image segmentation is a key task in neuroimaging studies. It is commonly conducted using standard computational tools, such as FSL, SPM, multi-atlas segmentation etc, which are often registration-based and suffer from expensive computation cost. Recently, there is an increased interest using deep neural networks for brain image segmentation, which have demonstrated advantages in both speed and performance. However, neural networks-based approaches normally require a large amount of manual annotations for optimising the massive amount of network parameters. For 3D networks used in volumetric image segmentation, this has become a particular challenge, as a 3D network consists of many more parameters compared to its 2D counterpart. Manual annotation of 3D brain images is extremely time-consuming and requires extensive involvement of trained experts. To address the challenge with limited manual annotations, here we propose a novel multi-task learning framework for brain image segmentation, which utilises a large amount of automatically generated partial annotations together with a small set of manually created full annotations for network training. Our method yields a high performance comparable to state-of-the-art methods for whole brain segmentation.
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Metadata
Title
Transfer Learning from Partial Annotations for Whole Brain Segmentation
Authors
Chengliang Dai
Yuanhan Mo
Elsa Angelini
Yike Guo
Wenjia Bai
Copyright Year
2019
DOI
https://doi.org/10.1007/978-3-030-33391-1_23

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