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Open Access

Combining Residual Attention Mechanisms and Generative Adversarial Networks for Hippocampus Segmentation

School of Information and Computer, Taiyuan University of Technology, Taiyuan 030600, China
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Abstract

This research discussed a deep learning method based on an improved generative adversarial network to segment the hippocampus. Different convolutional configurations were proposed to capture information obtained by a segmentation network. In addition, a generative adversarial network based on Pixel2Pixel was proposed. The generator was a codec structure combining a residual network and an attention mechanism to capture detailed information. The discriminator used a convolutional neural network to discriminate the segmentation results of the generated model and that of the expert. Through the continuously transmitted losses of the generator and discriminator, the generator reached the optimal state of hippocampus segmentation. T1-weighted magnetic resonance imaging scans and related hippocampus labels of 130 healthy subjects from the Alzheimer’s disease Neuroimaging Initiative dataset were used as training and test data; similarity coefficient, sensitivity, and positive predictive value were used as evaluation indicators. Results showed that the network model could achieve an efficient automatic segmentation of the hippocampus and thus has practical relevance for the correct diagnosis of diseases, such as Alzheimer’s disease.

Keywords

magnetic resonance imaginggenerative adversarial networkresidual networkattention mechanism

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Tsinghua Science and Technology
Volume 27 Issue 1,
February 2022
Pages 68-78
DOI: 10.26599/TST.2020.9010056
Cite this article:
Deng H, Zhang Y, Li R, et al. Combining Residual Attention Mechanisms and Generative Adversarial Networks for Hippocampus Segmentation. Tsinghua Science and Technology, 2022, 27(1): 68-78. https://doi.org/10.26599/TST.2020.9010056

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Received: 20 October 2020
Accepted: 17 November 2020
Published: 17 August 2021
© The author(s) 2022

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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