Auto-fader networks for harmonization on grey matter images

Chenwei Yan; Yuxing Hao; Dongyue Zhou; Yunge Zhang; Huanjie Li; Fengyu Cong

doi:10.1117/12.2687797

28 August 2023 Auto-fader networks for harmonization on grey matter images

Chenwei Yan, Yuxing Hao, Dongyue Zhou, Yunge Zhang, Huanjie Li, Fengyu Cong

Proceedings Volume 12724, Second International Conference on Biomedical and Intelligent Systems (IC-BIS 2023); 1272424 (2023) https://doi.org/10.1117/12.2687797
Event: Second International Conference on Biomedical and Intelligent Systems (IC-BIS2023), 2023, Xiamen, China

Abstract

Combining multi-site datasets is very important for increasing statistical power. With the development of medical imaging technology, the acquisition of MRI neuroimaging datasets with larger sample sizes has become possible. However, different scanners will generate non-biological noise in MRI neuroimaging data, leading to inconsistent research results across different scanners. In this research, we proposed the Auto-fader network to harmonize MRI neuroimaging data among different scanners to address this issue. The Fader network is a domain adaptation technique, it has an encoder-decoder architecture that can obtain the features without site effects, but the site labels must be encoded manually during training. Encoding site labels manually is only sometimes accurate. Therefore, on this basis, the Auto fader model has added site effects auto-encoding module and directly uses 3D convolution layers to extract features from the MRI neuroimaging data. Finally, experiments demonstrate that the Auto-fader model outperforms existing approaches in harmonizing MRI neuroimaging data from different scanners.

Citation Download Citation

Chenwei Yan, Yuxing Hao, Dongyue Zhou, Yunge Zhang, Huanjie Li, and Fengyu Cong "Auto-fader networks for harmonization on grey matter images", Proc. SPIE 12724, Second International Conference on Biomedical and Intelligent Systems (IC-BIS 2023), 1272424 (28 August 2023); https://doi.org/10.1117/12.2687797

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KEYWORDS

Magnetic resonance imaging

Neuroimaging

Data modeling

3D modeling

Scanners

Network architectures

Convolution

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