Abstract
Precise brain tissue segmentation is crucial for infant development tracking and early brain disorder diagnosis. However, it remains challenging to automatically segment the brain tissues of a 6-month-old infant (isointense phase), even for manual labeling, due to inherent ongoing myelination during the first postnatal year. The intensity contrast between gray matter and white matter is extremely low in isointense MRI data. To resolve this problem, in this study, we propose a novel network with multi-phase data and multi-scale assistance to accurately segment the brain tissues of the isointense phase. Specifically, our framework consists of two main modules, i.e., semantics-preserved generative adversarial network (SPGAN) and Transformer-based multi-scale segmentation network (TMSN). SPAGN bi-directionally transfers the brain appearance between the isointense phase and the adult-like phase. On the one hand, the synthesized isointense phase data augments the isointense dataset. On the other hand, the synthesized adult-like images provide prior knowledge to the ambiguous tissue boundaries in the paired isointense phase data. TMSN integrates features of multi-phase image pairs in a multi-scale manner, which exploits both the adult-like phase data, with much clearer boundaries as structural prior, and the surrounding tissues, with a larger receptive field to assist the isointense data tissue segmentation. Extensive experiments on the public dataset show that our proposed framework achieves significant improvement over the state-of-the-art methods quantitatively and qualitatively.
J. Liu and F. Liu—These authors contributed equally to this work.
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Acknowledgment
This work was supported in part by National Natural Science Foundation of China (No. 62131015 and 62203355), and Science and Technology Commission of Shanghai Municipality (STCSM) (No. 21010502600), and The Key R &D Program of Guangdong Province, China (No. 2021B0101420006).
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Liu, J. et al. (2023). Adult-Like Phase and Multi-scale Assistance for Isointense Infant Brain Tissue Segmentation. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14223. Springer, Cham. https://doi.org/10.1007/978-3-031-43901-8_6
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