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High-resolution deep transferred ASPPU-Net for nuclei segmentation of histopathology images

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Increasing cancer disease incidence worldwide has become a major public health issue. Manual histopathological analysis is a common diagnostic method for cancer detection. Due to the complex structure and wide variability in the texture of histopathology images, it has been challenging for pathologists to diagnose manually those images. Automatic segmentation of histopathology images to diagnose cancer disease is a continuous exploration field in recent times. Segmentation and analysis for diagnosis of histopathology images by using an efficient deep learning algorithm are the purpose of the proposed method.

Method

To improve the segmentation performance, we proposed a deep learning framework that consists of a high-resolution encoder path, an atrous spatial pyramid pooling bottleneck module, and a powerful decoder. Compared to the benchmark segmentation models having a deep and thin path, our network is wide and deep that effectively leverages the strength of residual learning as well as encoder–decoder architecture.

Results

We performed careful experimentation and analysis on three publically available datasets namely kidney dataset, Triple Negative Breast Cancer (TNBC) dataset, and MoNuSeg histopathology image dataset. We have used the two most preferred performance metrics called F1 score and aggregated Jaccard index (AJI) to evaluate the performance of the proposed model. The measured values of F1 score and AJI score are (0.9684, 0.9394), (0.8419, 0.7282), and (0.8344, 0.7169) on the kidney dataset, TNBC histopathology dataset, and MoNuSeg dataset, respectively.

Conclusion:

Our proposed method yields better results as compared to benchmark segmentation methods on three histopathology datasets. Visual segmentation results justify the high value of the F1 score and AJI scores which indicated that it is a very good prediction by our proposed model.

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Acknowledgements

The authors would like to thank the Editor-in-Chief and anonymous Reviewers for their constructive comments, which improved the quality of the manuscript.

Funding

This research work was supported in part by the Science Engineering and Research Board, Department of Science and Technology, Govt. of India under Grant No. EEG/2018/000323, 2019.

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Authors and Affiliations

  1. Department of E&C Engineering, National Institute of Technology Karnataka, Surathkal, Mangaluru, Karnataka, 575025, India

    Amit Kumar Chanchal & Shyam Lal

  2. Department of Pathology, Kasturba Medical College, Manipal Academy of Higher Education, Mangalore, Manipal, India

    Jyoti Kini

Authors
  1. Amit Kumar Chanchal
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  2. Shyam Lal
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Correspondence to Shyam Lal.

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The authors declare that they have no conflict of interest.

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We have used three publically available datasets.

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Chanchal, A.K., Lal, S. & Kini, J. High-resolution deep transferred ASPPU-Net for nuclei segmentation of histopathology images. Int J CARS 16, 2159–2175 (2021). https://doi.org/10.1007/s11548-021-02497-9

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  • DOI: https://doi.org/10.1007/s11548-021-02497-9

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