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RenSeg: Leveraging Unsupervised Segmentation Using Localization and Contour-Guided Quickshift for Renal Calculi and

Farhan Faruk, H M Sarwer Alam, Rafeed Rahman

    IEEE Journal of Biomedical and Health Informatics
    |December 9, 2025
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces RenSeg, an AI system for early kidney disease detection using unsupervised image segmentation. RenSeg significantly improves diagnostic accuracy for conditions like kidney stones and cancer, outperforming manual annotations.

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    Area of Science:

    • Medical Imaging
    • Artificial Intelligence
    • Nephrology

    Background:

    • Chronic kidney diseases pose a global health challenge, often leading to renal failure due to delayed diagnosis.
    • Shortage of nephrologists and scarcity of annotated data hinder timely diagnosis and treatment.
    • Existing AI methods predominantly use supervised learning, requiring extensive manual annotations.

    Purpose of the Study:

    • To develop an unsupervised AI system for early detection of kidney diseases.
    • To address the limitations of supervised learning methods in renal disease diagnosis.
    • To improve the accuracy and efficiency of kidney disease detection using AI.

    Main Methods:

    • Proposed RenSeg, an unsupervised contour-guided quickshift-based automated segmentation method for kidney localization.
    • Utilized a dataset of 8,737 axial and coronal CT-scan images.
    • Focused on detecting Renal Calculi and Renal Carcinoma.

    Main Results:

    • Unsupervised segmentation using RenSeg outperformed manually annotated datasets.
    • RenSeg achieved high Dice scores (0.9458 for calculi, 0.9309 for carcinoma), precision (0.95), and recall (0.94).
    • MobileNetV2 achieved 0.98 classification accuracy on RenSeg, surpassing manual annotations (0.92).
    • The unsupervised RenSeg approach demonstrated superior generalization across models.

    Conclusions:

    • Optimizing the region of interest (ROI) enhances predictive accuracy while reducing annotation effort.
    • RenSeg offers a scalable solution for timely renal disease detection.
    • Unsupervised learning methods show significant promise in overcoming data scarcity for AI-driven medical diagnostics.