ArticleBiomedical optics express2018
Deep longitudinal transfer learning-based automatic segmentation of photoreceptor ellipsoid zone defects on optical coherence tomography images of macular telangiectasia type 2.
Article in Biomedical optics express, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
36 citing papers in PubMed, 1 synthesis or guideline pooled it.
- In-depth analysis of research hotspots and emerging trends in AI for retinal diseases over the past decade.Frontiers in medicine · 2024Pooled it
- Impact of Baseline Quantitative OCT Features on Response to Risuteganib for the Treatment of Dry Age-Related Macular Degeneration: The Importance of Outer Retinal Integrity.Ophthalmology. Retina · 2022Trial
- VALIDATION OF A DEEP LEARNING-BASED ALGORITHM FOR SEGMENTATION OF THE ELLIPSOID ZONE ON OPTICAL COHERENCE TOMOGRAPHY IMAGES OF AN USH2A-RELATED RETINAL DEGENERATION CLINICAL TRIAL.Retina (Philadelphia, Pa.) · 2022Trial
- Longitudinal Assessment of Ellipsoid Zone Integrity, Subretinal Hyperreflective Material, and Subretinal Pigment Epithelium Disease in Neovascular Age-Related Macular Degeneration.Ophthalmology. Retina · 2021Trial
- Retinal cavitations in macular telangiectasia type 2 (MacTel): longitudinal structure-function correlations.The British journal of ophthalmology · 2021Trial
- Beyond Performance Metrics: Automatic Deep Learning Retinal OCT Analysis Reproduces Clinical Trial Outcome.Ophthalmology · 2020Trial
- Photoreceptor Disease at Ambiguous Transition Zones in Inherited Retinal Degenerations.Translational vision science & technology · 2025Article
- Validation of Inter-Reader Agreement/Consistency for Quantification of Ellipsoid Zone Integrity and Sub-RPE Compartmental Features Across Retinal Diseases.Diagnostics (Basel, Switzerland) · 2024Article
- Deep Learning-Assisted Measurements of Photoreceptor Ellipsoid Zone Area and Outer Segment Volume as Biomarkers for Retinitis Pigmentosa.Bioengineering (Basel, Switzerland) · 2023Article
- Microsurgery Robots: Applications, Design, and Development.Sensors (Basel, Switzerland) · 2023Review
- Longitudinal deep network for consistent OCT layer segmentation.Biomedical optics express · 2023Article
- Deep learning segmentation of the tear fluid reservoir under the sclera lens in optical coherence tomography images.Biomedical optics express · 2023Article
- Artificial intelligence-based strategies to identify patient populations and advance analysis in age-related macular degeneration clinical trials.Experimental eye research · 2022Article
- Deep learning-based classification and segmentation of retinal cavitations on optical coherence tomography images of macular telangiectasia type 2.The British journal of ophthalmology · 2022Article
- Performance of Deep Learning Models in Automatic Measurement of Ellipsoid Zone Area on Baseline Optical Coherence Tomography (OCT) Images From the Rate of Progression of USH2A-Related Retinal Degeneration (RUSH2A) Study.Frontiers in medicine · 2022Article
- Open-source deep learning-based automatic segmentation of mouse Schlemm's canal in optical coherence tomography images.Experimental eye research · 2022Article
- A Hybrid Model Composed of Two Convolutional Neural Networks (CNNs) for Automatic Retinal Layer Segmentation of OCT Images in Retinitis Pigmentosa (RP).Translational vision science & technology · 2021Article
- Challenges Associated With Ellipsoid Zone Intensity Measurements Using Optical Coherence Tomography.Translational vision science & technology · 2021Article
- Connectivity-based deep learning approach for segmentation of the epithelium inBiomedical optics express · 2021Article
- Deep Learning in Biomedical Optics.Lasers in surgery and medicine · 2021Review
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Authors and funding
5 authors.
Funding
Abstract
Photoreceptor ellipsoid zone (EZ) defects visible on optical coherence tomography (OCT) are important imaging biomarkers for the onset and progression of macular diseases. As such, accurate quantification of EZ defects is paramount to monitor disease progression and treatment efficacy over time. We developed and trained a novel deep learning-based method called Deep OCT Atrophy Detection (DOCTAD) to automatically segment EZ defect areas by classifying 3-dimensional A-scan clusters as normal or defective. Furthermore, we introduce a longitudinal transfer learning paradigm in which the algorithm learns from segmentation errors on images obtained at one time point to segment subsequent images with higher accuracy. We evaluated the performance of this method on 134 eyes of 67 subjects enrolled in a clinical trial of a novel macular telangiectasia type 2 (MacTel2) therapeutic agent. Our method compared favorably to other deep learning-based and non-deep learning-based methods in matching expert manual segmentations. To the best of our knowledge, this is the first automatic segmentation method developed for EZ defects on OCT images of MacTel2.
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