Abstract
The infant brain develops dramatically during the first two years of life. Accurate segmentation of brain tissues is essential to understand the early development of both normal and disease changes. However, the segmentation results of the same subject could demonstrate unexpectedly large variations across different time points, which may even lead to inaccurate and inconsistent results in charting infant brain development. In this paper, we propose a deep learning framework, which simultaneously exploits registration and segmentation for guaranteeing the longitudinal consistency among the segmentation results. Firstly, a manual label-guided registration model is designed to fast and accurately obtain the warped images from other time points. Secondly, a segmentation network with a longitudinal consistency constraint is developed to effectively obtain the temporal segmentation results. Thus, our proposed segmentation network could exploit the tissue information of warped intensity images from other time points to aid in segmenting the isointense phase (approximately 6–8 months) data, which is the most difficult case due to the low intensity contrast of tissues. Extensive experiments on infant brain images have shown improved performance achieved by our proposed method, compared with the existing state-of-the-art methods.
Y. Sun and J. 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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Sun, Y. et al. (2024). Consistent and Accurate Segmentation for Serial Infant Brain MR Images with Registration Assistance. In: Cao, X., Xu, X., Rekik, I., Cui, Z., Ouyang, X. (eds) Machine Learning in Medical Imaging. MLMI 2023. Lecture Notes in Computer Science, vol 14348. Springer, Cham. https://doi.org/10.1007/978-3-031-45673-2_19
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