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Yu, Z.* ; Han, X.* ; Zhang, S.* ; Feng, J.* ; Peng, T. ; Zhang, X.Y.*

MouseGAN++: Unsupervised disentanglement and contrastive representation for multiple MRI modalities synthesis and structural segmentation of mouse brain.

IEEE Trans. Med. Imaging 42, 1197-1209 (2022)
Postprint DOI
Open Access Green
Segmenting the fine structure of the mouse brain on magnetic resonance (MR) images is critical for delineating morphological regions, analyzing brain function, and understanding their relationships. Compared to a single MRI modality, multimodal MRI data provide complementary tissue features that can be exploited by deep learning models, resulting in better segmentation results. However, multimodal mouse brain MRI data is often lacking, making automatic segmentation of mouse brain fine structure a very challenging task. To address this issue, it is necessary to fuse multimodal MRI data to produce distinguished contrasts in different brain structures. Hence, we propose a novel disentangled and contrastive GAN-based framework, named MouseGAN++, to synthesize multiple MR modalities from single ones in a structure-preserving manner, thus improving the segmentation performance by imputing missing modalities and multi-modality fusion. Our results demonstrate that the translation performance of our method outperforms the state-of-the-art methods. Using the subsequently learned modality-invariant information as well as the modality-translated images, MouseGAN++ can segment fine brain structures with averaged dice coefficients of 90.0% (T2w) and 87.9% (T1w), respectively, achieving around +10% performance improvement compared to the state-of-the-art algorithms. Our results demonstrate that MouseGAN++, as a simultaneous image synthesis and segmentation method, can be used to fuse cross-modality information in an unpaired manner and yield more robust performance in the absence of multimodal data. We release our method as a mouse brain structural segmentation tool for free academic usage at https://github.com/yu02019.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Disentangled Representations ; Generative Adversarial Network ; Mouse Brain ; Mri ; Segmentation; Image
ISSN (print) / ISBN 0278-0062
e-ISSN 1558-254X
Journal IEEE Transactions on Medical Imaging
Quellenangaben Volume: 42, Issue: 4, Pages: 1197-1209 Article Number: , Supplement: ,
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Publishing Place New York, NY [u.a.]
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Helmholtz Artifical Intelligence Cooperation Unit (HAICU)
Grants Natural Science Foundation of Shanghai
ZJLab
Shanghai Municipal Science and Technology Major Project
National Natural Science Foundation of China