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Glomerulus Detection Using Segmentation Neural Networks

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Abstract

Digital pathology is vital for the correct diagnosis of kidney before transplantation or kidney disease identification. One of the key challenges in kidney diagnosis is glomerulus detection in kidney tissue segments. In this study, we propose a deep learning–based method for glomerulus detection from digitized kidney slide segments. The proposed method applies models based on convolutional neural networks to detect image segments containing the glomerulus region. We employ various networks such as ResNets, UNet, LinkNet, and EfficientNet to train the models. In our experiments on a network trained on the NIH HuBMAP kidney whole slide image dataset, the proposed method achieves the highest scores with Dice coefficient of 0.942.

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Data Availability

The results obtained in this paper are based upon data generated by the NIH Human BioMolecular Atlas Program (HuBMAP). The images used in the paper are available at https://portal.hubmapconsortium.org.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Graphic Era (Deemed to be University), Dehradun, 248002, Uttarkhand, India

    Surender Singh Samant, Arun Chauhan & Vijay Singh

  2. Department of Electronics and Communication Engineering, Indian Institute of Information Technology, Dharwad, Karnataka, 580009, India

    Jagadish DN

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  1. Surender Singh Samant
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  2. Arun Chauhan
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  3. Jagadish DN
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  4. Vijay Singh
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All the authors contributed equally to the study. All the authors read and approved the final manuscript.

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Correspondence to Surender Singh Samant.

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Singh Samant, S., Chauhan, A., DN, J. et al. Glomerulus Detection Using Segmentation Neural Networks. J Digit Imaging 36, 1633–1642 (2023). https://doi.org/10.1007/s10278-022-00764-y

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