Abstract
Accurate tissue segmentation of large-scale pediatric brain MR images from multiple sites is essential to characterize early brain development. Due to imaging motion/Gibbs artifacts and multi-site issue (or domain shift issue), it remains a challenge to accurately segment brain tissues from multi-site pediatric MR images. In this paper, we present a multi-scale self-supervised learning (M-SSL) framework to accurately segment tissues for multi-site pediatric brain MR images with artifacts. Specifically, we first work on the downsampled images to estimate coarse tissue probabilities and build a global anatomic guidance. We then train another segmentation model based on the original images to estimate fine tissue probabilities, which are further integrated with the global anatomic guidance to refine the segmentation results. In the testing stage, to alleviate the multi-site issue, we propose an iterative self-supervised learning strategy to train a site-specific segmentation model based on a set of reliable training samples automatically generated for a to-be-segmented site. The experimental results on pediatric brain MR images with real artifacts and multi-site subjects from the iSeg-2019 challenge demonstrate that our M-SSL method achieves better performance compared with several state-of-the-art methods.
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References
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Acknowledgements
This work was supported in part by National Institutes of Health grants MH109773 and MH117943, National Natural Science Foundation of China under Grant No. 61701192, 61872419, No.61873324, the Natural Science Foundation of Shandong Province, China, under Grant No. ZR2017QF004, No. ZR2019MF040, ZR2019MH106. This work utilizes approaches developed by an NIH grant (1U01MH110274) and the efforts of the UNC/UMN Baby Connectome Project Consortium.
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Sun, Y., Gao, K., Lin, W., Li, G., Niu, S., Wang, L. (2021). Multi-scale Self-supervised Learning for Multi-site Pediatric Brain MR Image Segmentation with Motion/Gibbs Artifacts. In: Lian, C., Cao, X., Rekik, I., Xu, X., Yan, P. (eds) Machine Learning in Medical Imaging. MLMI 2021. Lecture Notes in Computer Science(), vol 12966. Springer, Cham. https://doi.org/10.1007/978-3-030-87589-3_18
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