CU-Net: Cascaded U-Net with Loss Weighted Sampling for Brain Tumor Segmentation

  • Hongying Liu
  • Xiongjie Shen
  • Fanhua ShangEmail author
  • Feihang Ge
  • Fei Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11846)


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.


Brain tumor segmentation Cascaded U-Net Feature fusion Loss weighted sampling 



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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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Hongying Liu
    • 1
  • Xiongjie Shen
    • 1
  • Fanhua Shang
    • 1
    Email author
  • Feihang Ge
    • 2
  • Fei Wang
    • 3
  1. 1.Key Lab of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial IntelligenceXidian UniversityXi’anChina
  2. 2.School of Information Science and TechnologyAichi Prefectural UniversityNagakuteJapan
  3. 3.Weill Cornell Medical SchoolCornell UniversityNew YorkUSA

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