Automated Segmentation and Morphometry of Zebrafish Anterior Chamber OCT Scans

Zebrafish (Danio rerio) eyes are widely used in modeling studies of human ophthalmic diseases, including glaucoma and myopia. These pathologies cause morphological variations the anterior chamber elements, which can be quantitatively measured using morphometric parameters, such as corneal curvature, central thickness, angle. In present work, an automated method is presented for iris segmentation, well determination above-mentioned morphometry from optical coherence tomography (OCT) scans zebrafish. The proposed consists four stages; namely, preprocessing, postprocessing, extraction parameters. first stage composed a combination wavelet Fourier transforms gamma correction artifact removal/reduction. segmentation step achieved U-net convolutional neural network. postprocessing multilevel thresholding operations. Finally, three algorithms last step. morphology obtained our framework compared against manual measurements to assess effectiveness method. results show that scheme allows reliable thereby allowing efficient assessment massive on zebrafish OCT scans.