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Automatic Segmentation of Mitochondria from EM Images via Hierarchical Context Forest

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Book cover Recent Advances in Data Science (IDMB 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1099))

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Abstract

To solve the problem of automatic mitochondria segmentation from electron microscope (EM) images, a hierarchical context forest (HCF) model using multi-level context features was proposed. Exploring effective contextual information is crucial to address the challenges caused by the varied appearances and shapes of mitochondria, and the complicated image content. To this end, a novel class of features named local patch pattern (LPP) are designed to characterize local contextual information, which are used to resolve the ambiguity caused by similar appearances and intensities of different organelles. Furthermore, to capture long-range contextual information, we also extract LPP features on intermediate probability predictions of the HCF model. Moreover, a multiscale strategy is used to capture different sizes of mitochondria. Solid validations of our method conducted on public dataset demonstrated the effectiveness of both of the proposed LPP features and the proposed model. The result of comparison showed that, the proposed method achieved distinct improvement of results in terms of Precision, Recall and F1-value score.

J. Yi and Z. Yuan—These authors contribute equally to this paper.

J. Peng—Supported by National Natural Science Foundation of China (11771160), and Fujian Science and Technology Foundation (2019H0016).

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Authors and Affiliations

  1. College of Computer Science and Technology, Huaqiao University, Xiamen, 361021, China

    Jiajin Yi, Zhimin Yuan & Jialin Peng

Authors
  1. Jiajin Yi
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  2. Zhimin Yuan
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  3. Jialin Peng
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Corresponding author

Correspondence to Jialin Peng .

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Editors and Affiliations

  1. Fordham University, New York, NY, USA

    Henry Han

  2. Guangxi University, Nanning, China

    Tie Wei

  3. Guangzhou University, Guangzhou, China

    Wenbin Liu

  4. Jiangsu University, Zhenjiang, China

    Fei Han

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Yi, J., Yuan, Z., Peng, J. (2020). Automatic Segmentation of Mitochondria from EM Images via Hierarchical Context Forest. In: Han, H., Wei, T., Liu, W., Han, F. (eds) Recent Advances in Data Science. IDMB 2019. Communications in Computer and Information Science, vol 1099. Springer, Singapore. https://doi.org/10.1007/978-981-15-8760-3_16

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  • DOI: https://doi.org/10.1007/978-981-15-8760-3_16

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-8759-7

  • Online ISBN: 978-981-15-8760-3

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