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Automatic cell segmentation in histopathological images via two-staged superpixel-based algorithms

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Abstract

The analysis of cell characteristics from high-resolution digital histopathological images is the standard clinical practice for the diagnosis and prognosis of cancer. Yet, it is a rather exhausting process for pathologists to examine the cellular structures manually in this way. Automating this tedious and time-consuming process is an emerging topic of the histopathological image-processing studies in the literature. This paper presents a two-stage segmentation method to obtain cellular structures in high-dimensional histopathological images of renal cell carcinoma. First, the image is segmented to superpixels with simple linear iterative clustering (SLIC) method. Then, the obtained superpixels are clustered by the state-of-the-art clustering-based segmentation algorithms to find similar superpixels that compose the cell nuclei. Furthermore, the comparison of the global clustering-based segmentation methods and local region-based superpixel segmentation algorithms are also compared. The results show that the use of the superpixel segmentation algorithm as a pre-segmentation method improves the performance of the cell segmentation as compared to the simple single clustering-based segmentation algorithm. The true positive ratio (TPR), true negative ratio (TNR), F-measure, precision, and overlap ratio (OR) measures are utilized as segmentation performance evaluation. The computation times of the algorithms are also evaluated and presented in the study.

The visual flowchart of the proposed automatic cell segmentation in histopathological images via two-staged superpixel-based algorithms.

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Acknowledgements

This work was supported by the Scientific Research Projects Coordination Department, Yildiz Technical University, under Project 2014-04-01-KAP01. The authors also would like to thank Beck Laboratory at Harvard University for providing and annotating the high-resolution histopathological images of renal cell carcinoma data set selected from the Cancer Genome Atlas (TCGA) data portal and publicly available for academic usage. The authors state no conflicts of interest and have nothing to disclose.

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Authors and Affiliations

  1. Department of Computer Engineering, Yildiz Technical University (YTU), 34220, Istanbul, Turkey

    Abdulkadir Albayrak & Gokhan Bilgin

  2. Signal and Image Processing Lab. (SIMPLAB) in YTU, 34220, Istanbul, Turkey

    Abdulkadir Albayrak & Gokhan Bilgin

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  1. Abdulkadir Albayrak
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  2. Gokhan Bilgin
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Correspondence to Gokhan Bilgin.

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Albayrak, A., Bilgin, G. Automatic cell segmentation in histopathological images via two-staged superpixel-based algorithms. Med Biol Eng Comput 57, 653–665 (2019). https://doi.org/10.1007/s11517-018-1906-0

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  • DOI: https://doi.org/10.1007/s11517-018-1906-0

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