Predicting dark-field images of H&E-stained esophageal specimens

Arlia B., Dinc O. F., Turker M. B., Tozburun S.

Advances in Microscopic Imaging IV 2023, Munich, Almanya, 28 Haziran 2023, cilt.12630 identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası: 12630
  • Doi Numarası: 10.1117/12.2672202
  • Basıldığı Şehir: Munich
  • Basıldığı Ülke: Almanya
  • Anahtar Kelimeler: artificial intelligence, bright field microscopy, dark field microscopy, Generative adversarial network (GAN)
  • Dokuz Eylül Üniversitesi Adresli: Evet

Özet

The potential of laser-induced thermal therapy can be reassessed in treating abnormal mucosal tissues with advances in fiber optics, diode laser technology, and optical imaging modalities. In this context, studies optimizing a large parameter matrix (e.g., laser power, surface scanning speed, beam diameter, and irradiation duration) may be of interest. This study presents an artificial intelligence algorithm utilizing a generative adversarial network that predicts dark-field microscopy images from bright-field images of H&E-stained esophageal specimens. The calculated structural similarity index measurement between ground truth and the predicted dark-field image reaches an average of 74%. Also, the mean squared error is 0.7%.