Abstract
This paper proposes a novel cascaded U-Net for brain tumor segmentation. Inspired by the distinct hierarchical structure of brain tumor, we design a cascaded deep network framework, in which the whole tumor is segmented firstly and then the tumor internal substructures are further segmented. Considering that the increase of the network depth brought by cascade structures leads to a loss of accurate localization information in deeper layers, we construct between-net connections to link features at the same resolution and transmit the detailed information from shallow layers to the deeper layers. Then we present a loss weighted sampling (LWS) scheme to eliminate the issue of imbalanced data. Experimental results on the BraTS 2017 dataset show that our framework outperforms the state-of-the-art segmentation algorithms, especially in terms of segmentation sensitivity.
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Acknowledgments
This work was supported by the State Key Program of National Natural Science of China (No. 61836009), the Project supported the Foundation for Innovative Research Groups of the National Natural Science Foundation of China (No. 61621005), the Major Research Plan of the National Natural Science Foundation of China (Nos. 91438201 and 91438103), the Fund for Foreign Scholars in University Research and Teaching Programs (the 111 Project) (No. B07048), the National Natural Science Foundation of China (Nos. 61976164, 61876220, 61876221, U1701267, U1730109, 61473215, 61871310, 61472306, and 61502369), the Program for Cheung Kong Scholars and Innovative Research Team in University (No. IRT_15R53), the Science Foundation of Xidian University (Nos. 10251180018 and 10251180019), the Fundamental Research Funds for the Central Universities under Grant (No. 20101195989), the National Science Basic Research Plan in Shaanxi Province of China (No. 2019JQ-657), and the Key Special Project of China High Resolution Earth Observation System-Young Scholar Innovation Fund.
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Liu, H., Shen, X., Shang, F., Ge, F., Wang, F. (2019). CU-Net: Cascaded U-Net with Loss Weighted Sampling for Brain Tumor Segmentation. In: Zhu, D., et al. Multimodal Brain Image Analysis and Mathematical Foundations of Computational Anatomy. MBIA MFCA 2019 2019. Lecture Notes in Computer Science(), vol 11846. Springer, Cham. https://doi.org/10.1007/978-3-030-33226-6_12
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DOI: https://doi.org/10.1007/978-3-030-33226-6_12
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