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    A new Bi-Threshold Constrained Adaptive Scale (BTCAS) blob detector improves small object detection in medical images. This method enhances accuracy for imaging biomarkers by integrating U-Net and Difference of Gaussian (DoG) scale analysis.

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    Area of Science:

    • Medical Imaging
    • Computer Vision
    • Biomedical Engineering

    Background:

    • Medical imaging advances enable biomarker discovery for diagnosis and prognosis.
    • Accurate detection and segmentation of small objects (blobs) are crucial for quantitative biomarker measurement.
    • Challenges include low resolution, noise, and overlapping blobs in medical images.

    Purpose of the Study:

    • To develop a novel Bi-Threshold Constrained Adaptive Scale (BTCAS) blob detector.
    • To address limitations in detecting small, overlapping blobs in medical imaging.
    • To improve the accuracy of quantitative biomarker measurements.

    Main Methods:

    • Proposed a Bi-Threshold Constrained Adaptive Scale (BTCAS) blob detector.
    • Integrated U-Net probability thresholds with Difference of Gaussian (DoG) scale.
    • Utilized adaptive scale selection and Hessian convexity maps to resolve under-segmentation.

    Main Results:

    • BTCAS demonstrated superior performance compared to HDoG, U-Net variants, and UH-DoG.
    • Validated on simulated blobs, human kidney MRI, and mouse kidney MRI datasets.
    • Achieved statistically significant improvements in precision, recall, F-score, Dice, and IoU.

    Conclusions:

    • The BTCAS blob detector offers a statistically significant improvement for small object detection in medical imaging.
    • BTCAS effectively overcomes challenges posed by low resolution, noise, and blob overlap.
    • This method enhances the reliability of quantitative measurements for imaging biomarkers.