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Robust Frequency-Aware Instance Segmentation for Serial Tissue Sections

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Pattern Recognition (ACPR 2021)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13188))

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Abstract

Serial tissue sections are widely used in imaging large tissue volumes. Navigating to each section is indispensable in the automatic imaging process. Nowadays, the locations of sections are labeled manually or semi-manually. Sections are similar and the border is indiscernible if they stick together, which makes it difficult to locate the sections automatically. In this paper, we present frequency-aware instance segmentation framework (FANet), which can extract shape and size information of sections very well. Firstly, FANet uses discrete cosine transform (DCT) . Secondly, each channel extracts an specific frequency component of themselves. Frequency components from all channels is taken as the multi-frequency description of feature map and finally used to model the channel attention. Additionally, we propose a dataset about the serial sections as benchmark, which contains 2708 images in training set and 1193 images in validation set. Experimental results on the benchmark demonstrate our FANet achieves superior performance compared with the current methods. Our code and dataset will be made public.

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Acknowledgement

This research was funded by CAS Key Technology Talent Program (No. 292019000126 to X.C.)

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Authors and Affiliations

  1. National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Guodong Sun, Zejin Wang, Guoqing Li & Hua Han

  2. School of Artificial Intelligence, School of Future Technology, University of Chinese Academy of Sciences, Beijing, China

    Guodong Sun, Zejin Wang & Hua Han

  3. CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai, China

    Hua Han

Authors
  1. Guodong Sun
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  2. Zejin Wang
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  3. Guoqing Li
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  4. Hua Han
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Corresponding authors

Correspondence to Guoqing Li or Hua Han .

Editor information

Editors and Affiliations

  1. Korea University, Seoul, Korea (Republic of)

    Christian Wallraven

  2. Nanjing University, Nanjing, China

    Qingshan Liu

  3. Osaka University, Osaka, Japan

    Hajime Nagahara

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Sun, G., Wang, Z., Li, G., Han, H. (2022). Robust Frequency-Aware Instance Segmentation for Serial Tissue Sections. In: Wallraven, C., Liu, Q., Nagahara, H. (eds) Pattern Recognition. ACPR 2021. Lecture Notes in Computer Science, vol 13188. Springer, Cham. https://doi.org/10.1007/978-3-031-02375-0_28

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  • DOI: https://doi.org/10.1007/978-3-031-02375-0_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-02374-3

  • Online ISBN: 978-3-031-02375-0

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