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3D Ultrasound Spine Image Selection Using Convolution Learning-to-Rank Algorithm.

Juan Lyu, Sai Ho Ling, Sunetra Banerjee

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |January 18, 2020
    PubMed
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    A new convolutional RankNet algorithm automatically selects the best 3D ultrasound images for scoliosis assessment. This radiation-free method achieves 100% accuracy, improving diagnostic efficiency and reliability.

    Area of Science:

    • Medical Imaging
    • Artificial Intelligence
    • Spinal Health

    Background:

    • 3D Ultrasound imaging offers a real-time, cost-effective, and radiation-free method for scoliosis assessment.
    • Image quality varies across depths in 3D ultrasound, necessitating optimal coronal image selection for accurate diagnosis.
    • Manual image selection is subjective, time-consuming, and prone to variability.

    Purpose of the Study:

    • To develop an automated algorithm for selecting the best quality 3D ultrasound images for scoliosis assessment.
    • To address the limitations of manual image selection by introducing a data-driven approach.
    • To improve the accuracy and efficiency of scoliosis diagnosis using 3D ultrasound.

    Main Methods:

    • A learning-to-rank approach was employed, framing image selection as a ranking problem based on image quality.

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  • RankNet, a pairwise learning-to-rank algorithm, was utilized for automatic image ranking.
  • The traditional artificial neural network backbone of RankNet was replaced with a Convolutional Neural Network (CNN) to enhance feature extraction capabilities.

    • Main Results:

    • The proposed convolutional RankNet achieved 100% accuracy in selecting the optimal ultrasound images.
    • In comparison, conventional DenseNet models achieved only 35% accuracy for the same task.
    • The convolutional RankNet demonstrated superior performance in identifying high-quality ultrasound images from a set of mediocre ones.

    Conclusions:

    • The convolutional RankNet is highly effective for automatically selecting the best 3D ultrasound images for scoliosis assessment.
    • This automated approach significantly outperforms conventional methods in image quality assessment.
    • The developed algorithm enhances the reliability and efficiency of 3D ultrasound in clinical practice for scoliosis diagnosis.