📄 Empirical Study of Quality Image Assessment for Synthesis of Fetal Head Ultrasound Imaging with DCGANs

T. Bautista, J. Matthew, H. Kerdegari, L. Peralta and M. Xochicale

26th Conference on Medical Image Understanding and Analysis (MIUA 2022), Cambridge, 27-29 July 2022

(This work is 100% Reproducible)

Abstract

In this work, we present an empirical study of DCGANs, including hyperparameter heuristics and image quality assessment, as a way to address the scarcity of datasets to investigate fetal head ultrasound. We present experiments to show the impact of different image resolutions, epochs, dataset size input, and learning rates for quality image assessment on four metrics: mutual information (MI), Fr'echet inception distance (FID), peak-signal-to-noise ratio (PSNR), and local binary pattern vector (LBPv). The results show that FID and LBPv have stronger relationship with clinical image quality scores.

Poster

Medical Image Understanding and Analysis 🔗.

26th UK Conference on Medical Image Understanding and Analysis. 27 - 29 July 2022 at University of Cambridge.
See README.md for further information about miau2022 conference.

Licence and Citation

This work is under Creative Commons Attribution-Share Alike license . Hence, you are free to reuse it and modify it as much as you want and as long as you cite this work as original reference and you re-share your work under the same terms.

BibTeX to cite

@misc{https://doi.org/10.48550/arxiv.2206.01731,
  author = {Bautista, Thea and 
            Matthew, Jacqueline and 
            Kerdegari, Hamideh and 
            Peralta, Laura and 
            Xochicale, Miguel},
  title = {Empirical Study of Quality Image Assessment for Synthesis of Fetal Head Ultrasound Imaging with DCGANs},
  doi = {10.48550/ARXIV.2206.01731},
  url = {https://arxiv.org/abs/2206.01731},
  keywords = {Image and Video Processing (eess.IV), 
              Computer Vision and Pattern Recognition (cs.CV), 
              Machine Learning (cs.LG), 
              Medical Physics (physics.med-ph), 
              FOS: Electrical engineering, electronic engineering, information engineering, 
              FOS: Electrical engineering, electronic engineering, information engineering, 
              FOS: Computer and information sciences, 
              FOS: Computer and information sciences, 
              FOS: Physical sciences, 
              FOS: Physical sciences}, 
  publisher = {arXiv},
  year = {2022},
  copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International}
}

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Contributors

Thanks goes to all these people (emoji key):

Thea Bautista 💻 🤔 🔧

Jacqueline Matthew 🔬 🤔

Hamideh Kerdegari 🔬 🤔

Laura Peralta 🔬 🤔

Miguel Xochicale 💻 🔬 🤔 🔧 📖 🔧

This work follows the all-contributors specification.
Contributions of any kind welcome!

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