An increase in signal-to-noise ratio (SNR) and susceptibility-induced contrast at higher field strengths, e.g., 7T, is crucial for medical image analysis by providing better insights for the pathophysiology, diagnosis, and treatment of several disease entities. However, it is difficult to obtain 7T images in real clinical practices due to the high cost and low accessibility. In this paper, we propose a novel knowledge keeper network (KKN) to guide brain tissue segmentation by taking advantage of 7T representations without explicitly using 7T images. By extracting features of a 3T input image substantially and then transforming them to 7T features via knowledge distillation (KD), our method achieves deriving 7T-like representations from a given 3T image and exploits them for tissue segmentation. On two independent datasets, we evaluated our method’s validity in qualitative and quantitative manners on 7T-like image synthesis and 7T-guided tissue segmentation by comparing with the comparative methods in the literature.
CITATION STYLE
Lee, J., Oh, K., Shen, D., & Suk, H. I. (2022). A Novel Knowledge Keeper Network for 7T-Free but 7T-Guided Brain Tissue Segmentation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13435 LNCS, pp. 330–339). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-16443-9_32
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