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Link to original content: https://doi.org/10.1007/s11517-017-1630-1
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Cell segmentation in histopathological images with deep learning algorithms by utilizing spatial relationships

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Abstract

In many computerized methods for cell detection, segmentation, and classification in digital histopathology that have recently emerged, the task of cell segmentation remains a chief problem for image processing in designing computer-aided diagnosis (CAD) systems. In research and diagnostic studies on cancer, pathologists can use CAD systems as second readers to analyze high-resolution histopathological images. Since cell detection and segmentation are critical for cancer grade assessments, cellular and extracellular structures should primarily be extracted from histopathological images. In response, we sought to identify a useful cell segmentation approach with histopathological images that uses not only prominent deep learning algorithms (i.e., convolutional neural networks, stacked autoencoders, and deep belief networks), but also spatial relationships, information of which is critical for achieving better cell segmentation results. To that end, we collected cellular and extracellular samples from histopathological images by windowing in small patches with various sizes. In experiments, the segmentation accuracies of the methods used improved as the window sizes increased due to the addition of local spatial and contextual information. Once we compared the effects of training sample size and influence of window size, results revealed that the deep learning algorithms, especially convolutional neural networks and partly stacked autoencoders, performed better than conventional methods in cell segmentation.

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Acknowledgements

This research has been supported by Yildiz Technical University, Scientific Research Projects Coordination Department, Project Number: 2014-04-01-KAP01.

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Authors and Affiliations

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

    Gokhan Bilgin

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

    Nuh Hatipoglu & Gokhan Bilgin

  3. Graduate School of Natural and Applied Science, Yildiz Technical University, 34220, Istanbul, Turkey

    Nuh Hatipoglu

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  1. Nuh Hatipoglu
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  2. Gokhan Bilgin
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Correspondence to Gokhan Bilgin.

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Hatipoglu, N., Bilgin, G. Cell segmentation in histopathological images with deep learning algorithms by utilizing spatial relationships. Med Biol Eng Comput 55, 1829–1848 (2017). https://doi.org/10.1007/s11517-017-1630-1

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