A Deep Learning Approach to Digitally Stain Optical Coherence Tomography Images of the Optic Nerve Head - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Investigative Ophthalmology & Visual Science Année : 2018

A Deep Learning Approach to Digitally Stain Optical Coherence Tomography Images of the Optic Nerve Head

Sripad Krishna Devalla
  • Fonction : Auteur
Khai Sing Chin
  • Fonction : Auteur
Jean-Martial Mari
Tin A Tun
  • Fonction : Auteur
Nicholas G Strouthidis
  • Fonction : Auteur
Alexandre H Thiéry
  • Fonction : Auteur
  • PersonId : 1048090
Michaël J A Girard
  • Fonction : Auteur
  • PersonId : 1028419

Résumé

PURPOSE. To develop a deep learning approach to digitally stain optical coherence tomography (OCT) images of the optic nerve head (ONH). METHODS. A horizontal B-scan was acquired through the center of the ONH using OCT (Spectralis) for one eye of each of 100 subjects (40 healthy and 60 glaucoma). All images were enhanced using adaptive compensation. A custom deep learning network was then designed and trained with the compensated images to digitally stain (i.e., highlight) six tissue layers of the ONH. The accuracy of our algorithm was assessed (against manual segmentations) using the dice coefficient, sensitivity, specificity, intersection over union (IU), and accuracy. We studied the effect of compensation, number of training images, and performance comparison between glaucoma and healthy subjects. RESULTS. For images it had not yet assessed, our algorithm was able to digitally stain the retinal nerve fiber layer þ prelamina, the RPE, all other retinal layers, the choroid, and the peripapillary sclera and lamina cribrosa. For all tissues, the dice coefficient, sensitivity, specificity, IU, and accuracy (mean) were 0.84 6 0.03, 0.92 6 0.03, 0.99 6 0.00, 0.89 6 0.03, and 0.94 6 0.02, respectively. Our algorithm performed significantly better when compensated images were used for training (P < 0.001). Besides offering a good reliability, digital staining also performed well on OCT images of both glaucoma and healthy individuals. CONCLUSIONS. Our deep learning algorithm can simultaneously stain the neural and connective tissues of the ONH, offering a framework to automatically measure multiple key structural parameters of the ONH that may be critical to improve glaucoma management.
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Dates et versions

hal-02144350 , version 1 (30-05-2019)

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Citer

Sripad Krishna Devalla, Khai Sing Chin, Jean-Martial Mari, Tin A Tun, Nicholas G Strouthidis, et al.. A Deep Learning Approach to Digitally Stain Optical Coherence Tomography Images of the Optic Nerve Head. Investigative Ophthalmology & Visual Science, 2018, 59 (1), ⟨10.1167/iovs.17-22617⟩. ⟨hal-02144350⟩

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