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Found 11 papers, 0 papers with code
The 3D Structural Phenotype of the Glaucomatous Optic Nerve Head and its Relationship with The Severity of Visual Field Damage
In both approaches, we observed that structural changes were more prominent in the superior and inferior quadrant of the ONH, particularly in the RNFL, the prelamina, and the LC.
AI-based Clinical Assessment of Optic Nerve Head Robustness Superseding Biomechanical Testing
$\mathbf{Conclusions}$: We propose an AI-driven approach that can assess the robustness of a given ONH solely from a single OCT scan of the ONH, and without the need to perform biomechanical testing.
Geometric Deep Learning to Identify the Critical 3D Structural Features of the Optic Nerve Head for Glaucoma Diagnosis
To this end, we aimed: (1) To compare the performance of two relatively recent geometric deep learning techniques in diagnosing glaucoma from a single OCT scan of the ONH; and (2) To identify the 3D structural features of the ONH that are critical for the diagnosis of glaucoma.
Medical Application of Geometric Deep Learning for the Diagnosis of Glaucoma
Discussion: We provide a proof-of-principle for the application of geometric deep learning in the field of glaucoma.
The Three-Dimensional Structural Configuration of the Central Retinal Vessel Trunk and Branches as a Glaucoma Biomarker
Our work also suggested that the major retinal blood vessels form a skeleton -- the configuration of which may be representative of major ONH structural changes as typically observed with the development and progression of glaucoma.
Describing the Structural Phenotype of the Glaucomatous Optic Nerve Head Using Artificial Intelligence
We argue this is because the vast amount of information available in a 3D OCT scan of the ONH has not been fully exploited.
OCT-GAN: Single Step Shadow and Noise Removal from Optical Coherence Tomography Images of the Human Optic Nerve Head
Speckle noise and retinal shadows within OCT B-scans occlude important edges, fine textures and deep tissues, preventing accurate and robust diagnosis by algorithms and clinicians.
Towards Label-Free 3D Segmentation of Optical Coherence Tomography Images of the Optic Nerve Head Using Deep Learning
Since the introduction of optical coherence tomography (OCT), it has been possible to study the complex 3D morphological changes of the optic nerve head (ONH) tissues that occur along with the progression of glaucoma.
A Deep Learning Approach to Denoise Optical Coherence Tomography Images of the Optic Nerve Head
For all the ONH tissues, the mean CNR increased from $3. 50 \pm 0. 56$ (single-frame) to $7. 63 \pm 1. 81$ (denoised).
DRUNET: A Dilated-Residual U-Net Deep Learning Network to Digitally Stain Optic Nerve Head Tissues in Optical Coherence Tomography Images
Given that the neural and connective tissues of the optic nerve head (ONH) exhibit complex morphological changes with the development and progression of glaucoma, their simultaneous isolation from optical coherence tomography (OCT) images may be of great interest for the clinical diagnosis and management of this pathology.
A Deep Learning Approach to Digitally Stain Optical Coherence Tomography Images of the Optic Nerve Head
Purpose: To develop a deep learning approach to digitally-stain optical coherence tomography (OCT) images of the optic nerve head (ONH).
