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Robust Segmentation of Nucleus in Histopathology Images via Mask R-CNN

  • Xinpeng Xie
  • Yuexiang Li
  • Menglu Zhang
  • Linlin ShenEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11383)

Abstract

Nuclei segmentation plays an import role in histopathology images analysis. Deep learning approaches have shown its strength for histopathology images processing in various studies. In this paper, we proposed a novel deep learning framework for automatic nuclei segmentation. The framework adopts the Mask R-CNN as backbone and employs structure-preserving color normalization (SPCN) and watershed for pre- and post-processing. The proposed framework achieved a Dice score of 90.46% on the validation set, which demonstrates its competing segmentation performance.

Keywords

Nuclei segmentation SPCN Deep learning Instance segmentation 

Notes

Acknowledgement

The work was supported by Natural Science Foundation of China under grands no. 61672357, 61702339 and U1713214, and the Science and Technology Project of Guangdong Province (Grant No. 2018A050501014).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Xinpeng Xie
    • 1
  • Yuexiang Li
    • 2
  • Menglu Zhang
    • 1
  • Linlin Shen
    • 1
    Email author
  1. 1.Computer Vision InstituteShenzhen UniversityShenzhenChina
  2. 2.Youtu LabTencentShenzhenChina

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