An AI-based algorithmic system that predicts missing A-scans in cross-sectional retinal images

Dinc O. F., Arli B., Tozburun S.

Optical Coherence Imaging Techniques and Imaging in Scattering Media V 2023, Munich, Almanya, 25 - 29 Haziran 2023, cilt.12632 identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası: 12632
  • Doi Numarası: 10.1117/12.2671958
  • Basıldığı Şehir: Munich
  • Basıldığı Ülke: Almanya
  • Anahtar Kelimeler: frequency domain analysis, generative adversarial networks, image inpainting, machine learning, optical coherence tomography
  • Dokuz Eylül Üniversitesi Adresli: Evet

Özet

In this study, we present an artificial intelligence based algorithmic system that predicts missing A-scans of the edited OCT image by padding the A scan with zero. The developed artificial intelligence algorithmic system consists of two networks: convolutional neural network and generative adversarial network. The system theoretically suggests that skipping one-third of sequential A-scans and predicting the missing A-scan can increase the image acquisition rate by at least 33%. The structural similarity index measurement of the test data reaches an average of 82% between the ground truth images and the images predicted from the developed system. The mean squared error also is to 0.2%.