MimickNet, Matching Clinical Post-Processing Under Realistic Black-Box Constraints

@article{Huang2020MimickNetMC,
  title={MimickNet, Matching Clinical Post-Processing Under Realistic Black-Box Constraints},
  author={Ouwen Huang and Will Long and Nick Bottenus and Gregg E. Trahey and Sina Farsiu and Mark L. Palmeri},
  journal={IEEE transactions on medical imaging},
  year={2020}
}
  • Ouwen Huang, Will Long, +3 authors Mark L. Palmeri
  • Published in
    IEEE transactions on medical…
    2020
  • Computer Science, Medicine, Engineering, Mathematics
  • Image post-processing is used in clinical-grade ultrasound scanners to improve image quality (e.g., reduce speckle noise and enhance contrast). These post-processing techniques vary across manufacturers and are generally kept proprietary, which presents a challenge for researchers looking to match current clinical-grade workflows. We introduce a deep learning framework, MimickNet, that transforms conventional delay-and-summed (DAS) beams into the approximate Dynamic Tissue Contrast Enhanced… CONTINUE READING

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