AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Tsinghua Science and Technology Article
PDF (6 MB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
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
Show Author Information

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 imagingresidual networkattention mechanismgenerative adversarial network

References

[1]
S. Pei, J. Guan, and S. Zhou, Fusion analysis of resting-state networks and its lication to Alzheimer’s disease, Tsinghua Science and Technology, vol. 24, no. 4, pp. 456-467, 2019.
Crossref Google Scholar
[2]
D. L. Collins and J. C. Pruessner, Towards accurate, automatic segmentation of the hippocampus and amygdala from MRI by augmenting ANIMAL with a template library and label fusion, NeuroImage, vol. 52, no. 4, pp. 1355-1366,2010.
Crossref Google Scholar
[3]
A. R. Khan, N. Cherbuin, W. Wen, K. J. Anstey, P. Sachdev, and M. F. Beg, Optimal weights for local multi-atlas fusion using supervised learning and dynamic information (SuperDyn): Validation on hippocampus segmentation, NeuroImage, vol. 56, no. 1, pp. 126-139, 2011.
Crossref Google Scholar
[4]
F. Van der Lijn, T. den Heijer, M. M. B. Breteler, and W. J. Niessen, Hippocampus segmentation in MR images using atlas registration, voxel classification, and graph cuts, NeuroImage, vol. 43, no. 4, pp. 708-720, 2008.
Crossref Google Scholar
[5]
D. Zarpalas, P. Gkontra, P. Daras, and N. Maglaveras, Hippocampus segmentation by optimizing the local contribution of image and prior terms, through graph cuts and multi-atlas, in Proc. 9th Int. Symp. Biomedical Imaging (ISBI), Barcelona, Spain, 2012, pp. 1168-1171.
Crossref
[6]
M. Hajiesmaeili, B. Bagherinakhjavanlo, J. Dehmeshki, and T. Ellis, Segmentation of the Hippocampus for detection of Alzheimer’s disease, in Advances in Visual Computing, G. Bebis, R. Boyle, B. Parvin, D. Koracin, C. Fowlkes, S. Wang, M. H. Choi, S. Mantler, J. Schulze, D. Acevedo, et al., eds. Berlin, Germany: Springer, 2012, pp. 42-54.
[7]
S. Pei, J. Guan, and S. Zhou, Fusion analysis of resting-state networks and its lication to Alzheimer’s disease, Tsinghua Science and Technology, vol. 24, no. 4, pp. 456-467, 2019.
Crossref Google Scholar
[8]
E. A. A. Alaoui, S. C. K. Tekouabou, S. Hartini, Z. Rustam, H. Silkan, and S. Agoujil, Improvement in automated diagnosis of soft tissues tumors using machine learning, Big Data Mining and Analytics, vol. 4, no. 1, pp. 33-46, 2021.
Crossref Google Scholar
[9]
E. Shelhamer, J. Long, and T. Darrell, Fully convolutional networks for semantic segmentation, IEEE Trans. Pattern Anal. Mach. Intell., vol. 39, no. 4, pp. 640-651, 2017.
Crossref Google Scholar
[10]
O. Ronneberger, P. Fischer, and T. Brox, U-Net: Convolutional networks for biomedical image segmentation, in Proc. 18th Int. Conf. Medical Image Computing and Computer-Assisted Intervention MICCAI, Munich, Germany, 2015, pp. 234-241.
Crossref
[11]
Y. N. Chen, B. B. Shi, Z. W. Wang, P. Zhang, C. D. Smith, and J. D. Liu, Hippocampus segmentation through multi-view ensemble ConvNets, in Proc. 14thIEEE Int. Symp. Biomedical Imaging (ISBI 2017), Melbourne, Australia, 2017, pp. 192-196.
Crossref
[12]
Y. N. Chen, B. B. Shi, Z. W. Wang, T. Sun, C. D. Smith, and J. D. Liu, Accurate and consistent hippocampus segmentation through convolutional LSTM and view ensemble, in Int. Workshop on Machine Learning in Medical Imaging, Q. Wang, Y. H. Shi, H. I. Suk, and K. Suzuki, eds. Cham, Germany: Springer, 2017, pp. 88-96.
Crossref
[13]
L. Cao, L. Li, J. F. Zheng, X. Fan, F. Yin, H. Shen, and J. Zhang, Multi-task neural networks for joint hippocampus segmentation and clinical score regression, Multimed. Tools Appl., vol. 77, no. 22, pp. 29669-29686, 2018.
Crossref Google Scholar
[14]
Ö. Çiçek, A. Abdulkadir, S. S. Lienkamp, T. Brox, and O. Ronneberger, 3D U-Net: Learning dense volumetric segmentation from sparse annotation, in Medical Image Computing and Computer-Assisted Intervention, S. Ourselin, L. Joskowicz, M. R. Sabuncu, G. Unal, and W. Wells, eds. Cham, Germany: Springer, 2016, pp. 424-432.
[15]
I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, Generative adversarial nets, in Proc. 27th Int. Conf. Neural Information Processing Systems, Montreal, Canada, 2014, pp. 2672-2680.
[16]
M. Mirza and S. Osindero, Conditional generative adversarial nets, arXiv preprint arXiv:1411.1784, 2014.
Google Scholar
[17]
X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever, and P. Abbeel, InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets, in Proc. 30th Int. Conf. Neural Information Processing Systems, Barcelona, Spain, 2016, pp. 2180-2188.
[18]
M. Arjovsky, S. Chintala, and L. Bottou, Wasserstein generative adversarial networks, in Proc. 34th Int. Conf. Machine Learning (ICML), Sydney, Australia, vol. 70, 2017, pp. 214-223.
[19]
T. Che, Y. R. Li, A. P. Jacob, T. Bengio, and W. J. Li, Mode regularized generative adversarial networks, in Int. Conf. Learning Representations (ICLR), Toulon, France, 2017.
[20]
Z. Zhang, G. Fu, R. Ni, J. Liu, and X. Yang, A generative method for steganography by cover synthesis with auxiliary semantics, Tsinghua Science and Technology, vol. 25, no. 4, pp. 516-527, 2020.
Crossref Google Scholar
[21]
X. Wu, K. Xu, and P. Hall, A survey of image synthesis and editing with generative adversarial networks, Tsinghua Science and Technology, vol. 22, no. 6, pp. 660-674, 2017.
Crossref Google Scholar
[22]
P. Luc, C. Couprie, S. Chintala, and J. Verbeek, Semantic segmentation using adversarial networks, presented at Computer Science-Computer Vision and Pattern Recognition, NIPS Workshop on Adversarial Training, Barcelona, Spain, 2016.
[23]
Y. Xue, T. Xu, H. Zhang, L. R. Long, and X. L. Huang, SegAN: adversarial network with multi-scale L1 loss for medical image segmentation, Neuroinformatics, vol. 16, no. 3, pp. 383-392, 2018.
Crossref Google Scholar
[24]
T. Neff, C. Payer, D. Štern, and M. Urschler, Generative adversarial network based synthesis for supervised medical image segmentation, in OAGM & ARW Joint Workshop, Vienna, Austria, 2017.
[25]
A. K. Mondal, J. Dolz, and C Desrosiers, Few-shot 3d multi-modal medical image segmentation using generative adversarial learning, arXiv preprint arXiv: 1810.12241, 2018.
Google Scholar
[26]
G. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. W. M. van der Laak, B. Van Ginneken, and C. I. Sánchez, A survey on deep learning in medical image analysis, Med. Image Anal., vol. 42, pp. 60-88, 2017.
Crossref Google Scholar
[27]
B. Murugesan, K. Sarveswaran, R. S. Vijaya, S. M. Shankaranarayana, K. Ram, and M. Sivaprakasam, A context based deep learning approach for unbalanced medical image segmentation, presented at 2020 IEEE 17th Int. Symp. Biomedical Imaging (ISBI), Iowa City, IA, USA, 2020.
Crossref
[28]
S. Izadi, Z. Mirikharaji, J. Kawahara, and G. Hamarneh, Generative adversarial networks to segment skin lesions, presented at 2018 IEEE 15th Int. Symp. Biomedical Imaging (ISBI 2018), Washington, DC, USA, 2018, pp. 881-884.
Crossref
[29]
Z. J. Li, Y. Y. Wang, and J. H. Yu, Brain tumor segmentation using an adversarial network, in Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, A. Crimi, S. Bakas, H. Kuijf, B. Menze, and M. Reyes, eds. Cham, Germany: Springer, 2018, pp. 123-132.
[30]
Y. G. Shi, K. Cheng, and Z. W. Liu. Hippocampal subfields segmentation in brain MR images using generative adversarial networks, Biomed. Eng. Online, vol. 18, no. 1, p. 5, 2019.
Crossref Google Scholar
[31]
B. Hui, Y. Liu, J. Qiu, L. Cao, L. Ji, and Z. He, Study of texture segmentation and classification for grading small hepatocellular carcinoma based on CT images, Tsinghua Science and Technology, vol. 26, no. 2, pp. 199-207, 2021.
Crossref Google Scholar
[32]
J. Y. Zhu, T. Park, P. Isola, and A. A. Efros, Unpaired image-to-image translation using cycle-consistent adversarial networks, presented at 2017 IEEE Int. Conf. Computer Vision (ICCV), Venice, Italy, 2017, pp. 2242-2251.
Crossref
[33]
K. M. He, X. Y. Zhang, S. Q. Ren, and J. Sun, Deep residual learning for image recognition, presented at 2016 IEEE Conf. Computer Vision & Pattern Recognition, Las Vegas, NV, USA, 2016, pp. 770-778.
Crossref
[34]
J. Hu, L. Shen, S. Albanie, G. Sun, and E. H. Wu, Squeeze-and-excitation networks, IEEE Trans. Pattern Anal. Mach. Intell., vol. 42, no. 8, pp. 2011-2023, 2020.
Crossref Google Scholar
[35]
C. Szegedy, W. Liu, Y. Q. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, Going deeper with convolutions, in Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, 2015, pp. 1-9.
Crossref
[36]
T. Tong, R. Wolz, P. Coupé, J. V. Hajnal, and D. Rueckert, Segmentation of MR images via discriminative dictionary learning and sparse coding: Application to hippocampus labeling, NeuroImage, vol. 76, pp. 11-23, 2013.
Crossref Google Scholar
[37]
Y. T. Song, G. R. Wu, K. Bahrami, Q. S. Sun, and D. G. Shen, Progressive multi-atlas label fusion by dictionary evolution, Med. Image Anal., vol. 36, pp. 162-171, 2017.
Crossref Google Scholar
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

948

Views

124

Downloads

17

Crossref

12

Web of Science

20

Scopus

0

CSCD

Altmetrics
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/).
Return