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Ultrasound segmentation using U-Net: learning from simulated data and testing on real data.

Bahareh Behboodi, Hassan Rivaz

    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
    Summary
    This summary is machine-generated.

    Simulated ultrasound images can train deep learning models for image segmentation, proving effective for real-world data when labeled datasets are scarce. Envelope images yield superior segmentation results compared to B-mode images.

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    Area of Science:

    • Medical Imaging
    • Artificial Intelligence
    • Image Segmentation

    Background:

    • Ultrasound imaging is crucial for medical diagnosis and interventions due to its accessibility and low cost.
    • Manual segmentation of ultrasound images is time-consuming and labor-intensive.
    • Deep learning models require large labeled datasets, which are difficult to obtain for ultrasound images.

    Purpose of the Study:

    • To investigate the efficacy of using simulated ultrasound images for training deep learning segmentation models.
    • To evaluate the transferability of models trained on simulated data to real ultrasound images.
    • To compare the performance of segmentation using simulated envelope and B-mode ultrasound images.

    Main Methods:

    • A U-Net deep learning architecture was trained using simulated ultrasound images.
    • The trained model was tested on real tissue-mimicking phantom ultrasound data.
    • The study compared segmentation performance on simulated and real envelope and B-mode ultrasound images.

    Main Results:

    • Deep learning models trained on simulated ultrasound images demonstrated successful transferability to real ultrasound data.
    • Segmentation performance on real data validated the use of simulated data as an alternative training resource.
    • Envelope ultrasound images yielded superior segmentation results compared to B-mode images when tested on real phantom data.

    Conclusions:

    • Simulated ultrasound data can serve as a viable alternative for training deep learning models when real labeled datasets are unavailable.
    • The U-Net architecture trained on simulated data effectively segments real ultrasound images.
    • Envelope mode ultrasound imaging offers improved performance for deep learning-based segmentation tasks compared to B-mode imaging.