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Updated: May 29, 2026

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis
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Evaluating Iterative Deep Learning as a Labeling-Efficient Strategy for Tubular Segmentation in Digital

Borghild Tednes Larsen1,2, Yvan Samir Belakebi-Joly3, Maya Maya Barbosa Silva3

  • 1Department of Pathology, Haukeland University Hospital, Bergen, 5021, Norway. borghild.tednes.larsen@gmail.com.

Journal of Imaging Informatics in Medicine
|May 27, 2026
PubMed
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AI-powered annotation tools can significantly speed up the process of evaluating renal tubules for chronic kidney disease (CKD). Quick Annotator (QA) reduced manual annotation time by up to fivefold, aiding diagnosis and prognosis.

Area of Science:

  • Nephrology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Chronic kidney disease (CKD) presents a growing global health challenge.
  • Accurate morphological evaluation of renal tubules is crucial for CKD diagnosis and prognosis.
  • Manual annotation of renal tubules is time-consuming and labor-intensive, hindering large-scale analysis.

Purpose of the Study:

  • To rigorously evaluate the efficiency of annotation workflows using an iterative, model-assisted approach.
  • To quantify annotation speed-up in a consistent manner for renal tubule segmentation.
  • To assess the impact of Quick Annotator (QA) on annotation efficiency and segmentation quality.

Main Methods:

  • Implemented an iterative, model-assisted annotation strategy using the Quick Annotator (QA) tool.
Keywords:
Digital PathologyIterative deep learningKidney tubulesNephropathologySegmentationSpeed-up evaluation

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  • Trained QA models with varying annotation intervals (5, 10, 20 minutes) and refined them on diverse tubular lesion data.
  • Quantified annotation efficiency using multiple speed-up metrics and regression-based modeling, alongside segmentation quality assessment.

    • Main Results:

    • QA demonstrated significant improvements in annotation efficiency, especially with 10- and 20-minute annotation intervals.
    • Annotation time was reduced by up to a factor of five compared to manual annotation in QuPath.
    • Speed-up estimates varied across different evaluation methods, highlighting the importance of metric selection.
    • Segmentation performance was comparable to reference models for simpler cases but lower for complex, diseased tubules.

    Conclusions:

    • Iterative, model-assisted annotation with QA enhances annotation efficiency for renal tubule analysis.
    • The choice of evaluation metrics is critical for accurately interpreting annotation speed-up.
    • While QA shows promise, further development is needed to optimize performance for complex pathological conditions in CKD.