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A Fuzzy Segmentation Method to Learn Classification of Mitosis

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Abstract

Mitotic counts are widely used as a metric for cellular proliferation for prognosis and to determine the aggressiveness of individual cancers. This study presents a less labor-intensive method to count mitotic cells in breast cell sections. The proposed algorithm involves two phases: candidate segmentation and detection. During candidate segmentation, images are filtered through a blue ratio threshold to remove unnecessary background information and to increase the color difference between targets and non-targets for an entire digitized image. A fuzzy candidate segmentation method is used to adaptively determine threshold values in order to dichotomize gray-level images and distinguish the images of mitotic candidates from the background. The thresholding scheme integrates the spatial characteristics’ distribution in a histogram to determine an intensity threshold for the processed image, in order to filter insignificant information. During the detection phase, a two-class classification uses an attention mechanism that is realized by a set of fully connected neural networks, instead of convolutional layers, which decreases the computational cost. The validation test using ICPR2012 competition datasets shows that the proposed model outperforms current state-of-art techniques, in terms of the metrics, Accuracy, F1-score, and Precision and Recall.

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Funding

This research was supported by the Key Technology Research and Development Program of Zhejiang Province (No. 2017C03017); the Natural Science Foundation of China (Grants No. 11932017); Project of the regional diagnosis and treatment center of the Health Planning Committee (No. JBZX-201903); Zhejiang Provincial Natural Science Foundation of China under Grant No. LQ17H160008.

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Authors and Affiliations

  1. Department of Colorectal Surgery, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, China

    Maxwell Hwang, Da Wang, Xiang-Xing Kong & Kefeng Ding

  2. Cancer Institute (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences, Zhejiang Province, China), The Second Affiliated Hospital, Zhejiang University School of Medicine Zhejiang, Hangzhou, China

    Maxwell Hwang, Da Wang, Xiang-Xing Kong & Kefeng Ding

  3. Department of Hematology, The Fourth Affiliated Hospital of Zhejiang University School of Medicine, Yiwu, Zhejiang, China

    Cai Wu

  4. Department of Electrical Engineering, Tunghai University, Taichung, Taiwan, China

    Wei-Cheng Jiang

  5. Department of Electrical Engineering, National Sun Yat-sen University, Kaohsiung, 80424, Taiwan, China

    Kao-Shing Hwang

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  1. Maxwell Hwang
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  2. Da Wang
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  3. Cai Wu
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  4. Wei-Cheng Jiang
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  5. Xiang-Xing Kong
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  6. Kao-Shing Hwang
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  7. Kefeng Ding
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Corresponding authors

Correspondence to Maxwell Hwang or Kefeng Ding.

Additional information

Maxwell Hwang and Da Wang are the co-first authors.

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Hwang, M., Wang, D., Wu, C. et al. A Fuzzy Segmentation Method to Learn Classification of Mitosis. Int. J. Fuzzy Syst. 22, 1653–1664 (2020). https://doi.org/10.1007/s40815-020-00868-z

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  • DOI: https://doi.org/10.1007/s40815-020-00868-z

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