ArticleTranslational vision science & technology2022
Development of a β-Variational Autoencoder for Disentangled Latent Space Representation of Anterior Segment Optical Coherence Tomography Images.
Article in Translational vision science & technology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Generative AI as a transformational logic for cognitive neuroscience.Communications biology · 2026Review
- Artificial Second Harmonic Generation Image Feature Tuning with Generative Models and SeFa Semantic Analysis.JPhys photonics · 2026Article
- The application of artificial intelligence in glaucoma diagnosis and prediction.Frontiers in cell and developmental biology · 2023Review
- Development of Cumulative Order-Preserving Image Transformation Based Variational Autoencoder for Anterior Segment Optical Coherence Tomography Images.Translational vision science & technology · 2022Article
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5 authors.
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Abstract
purposeTo investigate the feasibility of extracting a low-dimensional latent structure of anterior segment optical coherence tomography (AS-OCT) images by use of a β-variational autoencoder (β-VAE).
methodsWe retrospectively collected 2111 AS-OCT images from 2111 eyes of 1261 participants from the ongoing Asan Glaucoma Progression Study. After hyperparameter optimization, the images were analyzed with β-VAE.
resultsThe mean participant age was 64.4 years, with mean values of visual field index and mean deviation of 86.4% and -5.33 dB, respectively. After experiments, a latent space size of 6 and β value of 53 were selected for latent space analysis with β-VAE. Latent variables were successfully disentangled, showing readily interpretable distinct characteristics, such as the overall depth and area of the anterior chamber (η1), pupil diameter (η2), iris profile (η3 and η4), and corneal curvature (η5).
conclusionsβ-VAE can successfully be applied for disentangled latent space representation of AS-OCT images, revealing the high possibility of applying unsupervised learning in the medical image analysis. TRANSLATIONAL RELEVANCE: This study demonstrates that a deep learning-based latent space model can be applied for the analysis of AS-OCT images.
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