Powered by OpenAIRE graph
Found an issue? Give us feedback
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Frontiers in Neurosc...arrow_drop_down
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
Frontiers in Neuroscience
Article
License: CC BY
Data sources: UnpayWall
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
Frontiers in Neuroscience
Article . 2021
License: CC BY
Data sources: Crossref
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
DOAJ-Articles
Article . 2021
Data sources: DOAJ-Articles
versions View all 4 versions
addClaim

This Research product is the result of merged Research products in OpenAIRE.

You have already added 0 works in your ORCID record related to the merged Research product.
addClaim

This Research product is the result of merged Research products in OpenAIRE.

You have already added 0 works in your ORCID record related to the merged Research product.

Longitudinal Prediction of Infant MR Images With Multi-Contrast Perceptual Adversarial Learning

Authors: Liying Peng; Liying Peng; Lanfen Lin; Yusen Lin; Yen-wei Chen; Zhanhao Mo; Roza M. Vlasova; +23 Authors

Longitudinal Prediction of Infant MR Images With Multi-Contrast Perceptual Adversarial Learning

Abstract

The infant brain undergoes a remarkable period of neural development that is crucial for the development of cognitive and behavioral capacities (Hasegawa et al., 2018). Longitudinal magnetic resonance imaging (MRI) is able to characterize the developmental trajectories and is critical in neuroimaging studies of early brain development. However, missing data at different time points is an unavoidable occurrence in longitudinal studies owing to participant attrition and scan failure. Compared to dropping incomplete data, data imputation is considered a better solution to address such missing data in order to preserve all available samples. In this paper, we adapt generative adversarial networks (GAN) to a new application: longitudinal image prediction of structural MRI in the first year of life. In contrast to existing medical image-to-image translation applications of GANs, where inputs and outputs share a very close anatomical structure, our task is more challenging as brain size, shape and tissue contrast vary significantly between the input data and the predicted data. Several improvements over existing GAN approaches are proposed to address these challenges in our task. To enhance the realism, crispness, and accuracy of the predicted images, we incorporate both a traditional voxel-wise reconstruction loss as well as a perceptual loss term into the adversarial learning scheme. As the differing contrast changes in T1w and T2w MR images in the first year of life, we incorporate multi-contrast images leading to our proposed 3D multi-contrast perceptual adversarial network (MPGAN). Extensive evaluations are performed to assess the qualityand fidelity of the predicted images, including qualitative and quantitative assessments of the image appearance, as well as quantitative assessment on two segmentation tasks. Our experimental results show that our MPGAN is an effective solution for longitudinal MR image data imputation in the infant brain. We further apply our predicted/imputed images to two practical tasks, a regression task and a classification task, in order to highlight the enhanced task-related performance following image imputation. The results show that the model performance in both tasks is improved by including the additional imputed data, demonstrating the usability of the predicted images generated from our approach.

Subjects by Vocabulary

Microsoft Academic Graph classification: Computer science media_common.quotation_subject Fidelity Task (project management) Neuroimaging Segmentation Imputation (statistics) media_common business.industry Contrast (statistics) Pattern recognition Missing data Term (time) Artificial intelligence business

Keywords

autism, Neurosciences. Biological psychiatry. Neuropsychiatry, imputation, postnatal brain development, Original Research, General Neuroscience, infant, longitudinal prediction, machine learning, generative adversarial networks, RC321-571, Neuroscience, MRI

58 references, page 1 of 6

Armanious, K., Jiang, C., Fischer, M., Küstner, T., Hepp, T., Nikolaou, K., et al. (2020). MedGAN: medical image translation using GANs. Comput. Med. Imaging Graph. 79:101684. doi: 10.1016/j.compmedimag.2019.101684

Ben-Cohen, A., Klang, E., Raskin, S. P., Amitai, M. M., and Greenspan, H. (2017). “Virtual pet images from ct data using deep convolutional networks: initial results,” in International Workshop on Simulation and Synthesis in Medical Imaging (Quebec, QC: Springer), 49-57. doi: 10.1007/978-3-319-68127-6_6 [OpenAIRE]

Bi, L., Kim, J., Kumar, A., Feng, D., and Fulham, M. (2017). “Synthesis of positron emission tomography (PET) images via multi-channel generative adversarial networks (GANs),” in Molecular Imaging, Reconstruction and Analysis of Moving Body Organs, and Stroke Imaging and Treatment (Quebec, QC: Springer), 43-51. doi: 10.1007/978-3-319-67564-0_5

Bowles, C., Gunn, R., Hammers, A., and Rueckert, D. (2018). “Modelling the progression of Alzheimer's disease in MRI using generative adversarial networks,” in Medical Imaging 2018: Image Processing (Houston, TX: International Society for Optics and Photonics).

Chen, T., and Guestrin, C. (2016). “Xgboost: a scalable tree boosting system,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (San Francisco, CA), 785-794. doi: 10.1145/2939672.2939785

Choi, H., and Lee, D. S. (2018). Generation of structural MR images from amyloid pet: application to MR-less quantification. J. Nuclear Med. 59, 1111-1117. doi: 10.2967/jnumed.117.199414

Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., Brox, T., and Ronneberger, O. (2016). “3D U-Net: learning dense volumetric segmentation from sparse annotation,” in International Conference on Medical Image Computing and ComputerAssisted Intervention (Athens: Springer), 424-432.

Dar, S. U., Yurt, M., Karacan, L., Erdem, A., Erdem, E., and Çukur, T. (2019). Image synthesis in multi-contrast MRI with conditional generative adversarial networks. IEEE Trans. Med. Imaging 38, 2375-2388. doi: 10.1109/TMI.2019.2901750

Emami, H., Aliabadi, M. M., Dong, M., and Chinnam, R. B. (2021). SPAGAN: spatial attention GAN for image-to-image translation. In: IEEE Transactions on Multimedia. Vol. 23, 391-401. doi: 10.1109/TMM.2020.2 975961

Fishbaugh, J., Prastawa, M., Gerig, G., and Durrleman, S. (2013). “Geodesic shape regression in the framework of currents,” in International Conference on Information Processing in Medical Imaging (Asilomar, CA: Springer), 718-729. doi: 10.1007/978-3-642-38868-2_60 [OpenAIRE]

  • BIP!
    Impact byBIP!
    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    3
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
  • citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    3
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
    Powered byBIP!BIP!
Powered by OpenAIRE graph
Found an issue? Give us feedback
citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
3
Top 10%
Average
Average
Funded byView all
NIH| UNC BIRCWH Career Development Program
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 5K12HD001441-07
  • Funding stream: EUNICE KENNEDY SHRIVER NATIONAL INSTITUTE OF CHILD HEALTH &HUMAN DEVELOPMENT
,
NIH| The Intellectual and Developmental Disabilities Research Center at CHOP/Penn
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 1U54HD086984-01
  • Funding stream: EUNICE KENNEDY SHRIVER NATIONAL INSTITUTE OF CHILD HEALTH & HUMAN DEVELOPMENT
iis
,
NIH| Genetic Liability for Autism and Infant Brain and Behavioral Development
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 5K01MH122779-03
  • Funding stream: NATIONAL INSTITUTE OF MENTAL HEALTH
iis
,
NIH| Postdoctoral Research in Neurodevelopmental Disorders
Project
  • Funder: National Institutes of Health (NIH)
  • Project Code: 5T32HD040127-05
  • Funding stream: EUNICE KENNEDY SHRIVER NATIONAL INSTITUTE OF CHILD HEALTH &HUMAN DEVELOPMENT
iis
Related to Research communities
EBRAINS
moresidebar

Do the share buttons not appear? Please make sure, any blocking addon is disabled, and then reload the page.