251 research outputs found
A feature agnostic approach for glaucoma detection in OCT volumes
Optical coherence tomography (OCT) based measurements of retinal layer
thickness, such as the retinal nerve fibre layer (RNFL) and the ganglion cell
with inner plexiform layer (GCIPL) are commonly used for the diagnosis and
monitoring of glaucoma. Previously, machine learning techniques have utilized
segmentation-based imaging features such as the peripapillary RNFL thickness
and the cup-to-disc ratio. Here, we propose a deep learning technique that
classifies eyes as healthy or glaucomatous directly from raw, unsegmented OCT
volumes of the optic nerve head (ONH) using a 3D Convolutional Neural Network
(CNN). We compared the accuracy of this technique with various feature-based
machine learning algorithms and demonstrated the superiority of the proposed
deep learning based method.
Logistic regression was found to be the best performing classical machine
learning technique with an AUC of 0.89. In direct comparison, the deep learning
approach achieved a substantially higher AUC of 0.94 with the additional
advantage of providing insight into which regions of an OCT volume are
important for glaucoma detection.
Computing Class Activation Maps (CAM), we found that the CNN identified
neuroretinal rim and optic disc cupping as well as the lamina cribrosa (LC) and
its surrounding areas as the regions significantly associated with the glaucoma
classification. These regions anatomically correspond to the well established
and commonly used clinical markers for glaucoma diagnosis such as increased cup
volume, cup diameter, and neuroretinal rim thinning at the superior and
inferior segments.Comment: 13 pages,3 figure
Retinal Optic Disc Segmentation using Conditional Generative Adversarial Network
This paper proposed a retinal image segmentation method based on conditional
Generative Adversarial Network (cGAN) to segment optic disc. The proposed model
consists of two successive networks: generator and discriminator. The generator
learns to map information from the observing input (i.e., retinal fundus color
image), to the output (i.e., binary mask). Then, the discriminator learns as a
loss function to train this mapping by comparing the ground-truth and the
predicted output with observing the input image as a condition.Experiments were
performed on two publicly available dataset; DRISHTI GS1 and RIM-ONE. The
proposed model outperformed state-of-the-art-methods by achieving around 0.96%
and 0.98% of Jaccard and Dice coefficients, respectively. Moreover, an image
segmentation is performed in less than a second on recent GPU.Comment: 8 pages, Submitted to 21st International Conference of the Catalan
Association for Artificial Intelligence (CCIA 2018
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