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PIS-Net: A Novel Pixel Interval Sampling Network for Dense Microorganism Counting in Microscopic Images

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Information Technology in Biomedicine (ITIB 2022)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1429))

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Abstract

A novel Pixel Interval Sampling Network (PIS-Net) is applied here for dense microorganism counting. The PIS-Net is designed for microorganism image segmentation with encoder to decoder architecture, and then the connected domain detection is applied for counting. The proposed method has good response for edge segmentation between tiny objects. Several classical segmentation metrics (Dice, Jaccard, and Hausdorff distance) are applied for evaluation. Experimental result shows that the proposed PIS-Net has the best performance and potential for dense tiny object counting tasks, which achieves \(96.88\%\) counting accuracy on the dataset with 420 yeast cell images. By comparing with the state-of-the-art approaches like Attention U-Net, Swin U-Net, and Trans U-Net, the proposed PIS-Net can segment the dense tiny objects with clearer boundaries and fewer incorrect debris, which shows the great potential of PIS-Net in the task of accurate counting tasks.

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Acknowledgement

This work is supported by “National Natural Science Foundation of China” (No. 61806047).

Author information

Authors and Affiliations

  1. Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China

    Jiawei Zhang & Chen Li

  2. Shengjing Hospital, China Medical University, Shenyang, China

    Hongzan Sun

  3. Institute for Medical Informatics, University of Luebeck, Lübeck, Germany

    Marcin Grzegorzek

Authors
  1. Jiawei Zhang
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  2. Chen Li
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  3. Hongzan Sun
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  4. Marcin Grzegorzek
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Corresponding author

Correspondence to Chen Li .

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Editors and Affiliations

  1. Faculty of Biomedical Engineering, Silesian University of Technology, Gliwice, Poland

    Ewa Pietka

  2. Faculty of Biomedical Engineering, Silesian University of Technology, Gliwice, Poland

    Pawel Badura

  3. Faculty of Biomedical Engineering, Silesian University of Technology, Gliwice, Poland

    Jacek Kawa

  4. Faculty of Biomedical Engineering, Silesian University of Technology, Gliwice, Poland

    Wojciech Wieclawek

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Zhang, J., Li, C., Sun, H., Grzegorzek, M. (2022). PIS-Net: A Novel Pixel Interval Sampling Network for Dense Microorganism Counting in Microscopic Images. In: Pietka, E., Badura, P., Kawa, J., Wieclawek, W. (eds) Information Technology in Biomedicine. ITIB 2022. Advances in Intelligent Systems and Computing, vol 1429. Springer, Cham. https://doi.org/10.1007/978-3-031-09135-3_26

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