Deep learning algorithms to isolate and quantify the structures of the anterior segment in optical coherence tomography images

  title={Deep learning algorithms to isolate and quantify the structures of the anterior segment in optical coherence tomography images},
  author={Tan Hung Pham and Sripad Krishna Devalla and A Ang and Zhi Da Soh and Alexandre Hoang Thiery and Craig Boote and Ching-Yu Cheng and Michael J. A. Girard and Victor Tc Koh},
  journal={British Journal of Ophthalmology},
  pages={1231 - 1237}
Background/Aims Accurate isolation and quantification of intraocular dimensions in the anterior segment (AS) of the eye using optical coherence tomography (OCT) images is important in the diagnosis and treatment of many eye diseases, especially angle-closure glaucoma. Method In this study, we developed a deep convolutional neural network (DCNN) for the localisation of the scleral spur; moreover, we introduced an information-rich segmentation approach for this localisation problem. An ensemble… 
Reproducibility of deep learning based scleral spur localisation and anterior chamber angle measurements from anterior segment optical coherence tomography images
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An overview of AI applications to the anterior segment addressing keratoconus, infectious keratitis, refractive surgery, corneal transplant, adult and paediatric cataracts, angle-closure glaucoma and iris tumour is provided and important clinical considerations for adoption of AI technologies, potential integration with telemedicine and future directions are highlighted.
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S Sectoral anatomical variations in angle closure eyes are easily misrepresented based on current AS-OCT imaging conventions, so a revised multi-image approach can better capture the mean and range of biometric measurements.


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The authors' 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.
DRUNET: A Dilated-Residual U-Net Deep Learning Network to Digitally Stain Optic Nerve Head Tissues in Optical Coherence Tomography Images
A robust segmentation framework is offered that could also be extended to the 3D segmentation of the ONH tissues and automatically extracted six clinically relevant neural and connective tissue structural parameters from the segmented tissues.
Assessment of the scleral spur in anterior segment optical coherence tomography images.
The inability to detect the scleral spur may hamper quantitative analysis of anterior chamber angle parameters that are dependent on the location of this anatomical structure, particularly in the superior and inferior quadrants.
The Effect of Scleral Spur Identification Methods on Structural Measurements by Anterior Segment Optical Coherence Tomography
The CM method was the most successful and least variable method of SS marking, which was more difficult in narrow angle and brown eyes, and had a large effect on angle parameters and iris concavity ratio.
U-Net: Convolutional Networks for Biomedical Image Segmentation
It is shown that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.
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A two-stage task-oriented deep learning method to detect large-scale anatomical landmarks simultaneously in real time, using limited training data, consisting of two deep convolutional neural networks, with each focusing on one specific task.
Association of narrow angles with anterior chamber area and volume measured with anterior-segment optical coherence tomography.
Smaller anterior chamber area and anterior chamber volume were independently associated with narrow angles in Singaporeans, even after controlling for other known ocular risk factors.
Reproducibility of Scleral Spur Identification and Angle Measurements Using Fourier Domain Anterior Segment Optical Coherence Tomography
Purpose. To evaluate intraobserver and interobserver agreement in locating the scleral spur landmark (SSL) and anterior chamber angle measurements obtained using Fourier Domain Anterior Segment