ArticleJournal of digital imaging2023
Glomerulus Detection Using Segmentation Neural Networks.
Article in Journal of digital imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 17 citations in OpenAlex.
- Multiscale segmentation using hierarchical phase-contrast tomography and deep learning.PLoS computational biology · 2026Article
- GlomNet: glomeruli segmentation network for WSI with diverse staining protocols for kidney disease diagnosis.Frontiers in medicine · 2026Article
- CapNet: An Automatic Attention-Based with Mixer Model for Cardiovascular Magnetic Resonance Image Segmentation.Journal of imaging informatics in medicine · 2025Article
- Leveraging advanced feature extraction for improved kidney biopsy segmentation.Frontiers in medicine · 2025Article
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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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