An AS-OCT image dataset for deep learning-enabled segmentation and 3D reconstruction for keratitis

Sci Data. 2024 Jun 13;11(1):627. doi: 10.1038/s41597-024-03464-0.

Abstract

Infectious keratitis is among the major causes of global blindness. Anterior segment optical coherence tomography (AS-OCT) images allow the characterizing of cross-sectional structures in the cornea with keratitis thus revealing the severity of inflammation, and can also provide 360-degree information on anterior chambers. The development of image analysis methods for such cases, particularly deep learning methods, requires a large number of annotated images, but to date, there is no such open-access AS-OCT image repository. For this reason, this work provides a dataset containing a total of 1168 AS-OCT images of patients with keratitis, including 768 full-frame images (6 patients). Each image has associated segmentation labels for lesions and cornea, and also labels of iris for full-frame images. This study provides a great opportunity to advance the field of image analysis on AS-OCT images in both two-dimensional (2D) and three-dimensional (3D) and would aid in the development of artificial intelligence-based keratitis management.

Publication types

  • Dataset

MeSH terms

  • Cornea / diagnostic imaging
  • Deep Learning*
  • Humans
  • Image Processing, Computer-Assisted
  • Imaging, Three-Dimensional
  • Keratitis* / diagnostic imaging
  • Tomography, Optical Coherence*