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Deep learning-based classification and segmentation of retinal cavitations on optical coherence tomography images of macular telangiectasia type 2
  1. Jessica Loo1,
  2. Cindy X Cai2,
  3. John Choong2,
  4. Emily Y Chew3,
  5. Martin Friedlander4,5,
  6. Glenn J Jaffe2,
  7. Sina Farsiu1,2
  1. 1 Biomedical Engineering, Duke University, Durham, North Carolina, USA
  2. 2 Ophthalmology, Duke Medicine, Durham, North Carolina, USA
  3. 3 Epidemiology and Clinical Applications, National Eye Institute, National Institutes of Health, Bethesda, Maryland, USA
  4. 4 The Lowy Medical Research Institute, La Jolla, California, USA
  5. 5 Molecular Medicine, The Scripps Research Institute, La Jolla, California, USA
  1. Correspondence to Jessica Loo, Biomedical Engineering, Duke University, Durham, NC 27708, USA; jessica.loo{at}


Aim To develop a fully automatic algorithm to segment retinal cavitations on optical coherence tomography (OCT) images of macular telangiectasia type 2 (MacTel2).

Methods The dataset consisted of 99 eyes from 67 participants enrolled in an international, multicentre, phase 2 MacTel2 clinical trial (NCT01949324). Each eye was imaged with spectral-domain OCT at three time points over 2 years. Retinal cavitations were manually segmented by a trained Reader and the retinal cavitation volume was calculated. Two convolutional neural networks (CNNs) were developed that operated in sequential stages. In the first stage, CNN1 classified whether a B-scan contained any retinal cavitations. In the second stage, CNN2 segmented the retinal cavitations in a B-scan. We evaluated the performance of the proposed method against alternative methods using several performance metrics and manual segmentations as the gold standard.

Results The proposed method was computationally efficient and accurately classified and segmented retinal cavitations on OCT images, with a sensitivity of 0.94, specificity of 0.80 and average Dice similarity coefficient of 0.94±0.07 across all time points. The proposed method produced measurements that were highly correlated with the manual measurements of retinal cavitation volume and change in retinal cavitation volume over time.

Conclusion The proposed method will be useful to help clinicians quantify retinal cavitations, assess changes over time and further investigate the clinical significance of these early structural changes observed in MacTel2.

  • imaging
  • retina

Data availability statement

No data are available. N/A.

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Data availability statement

No data are available. N/A.

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  • Contributors JL and SF designed the study. EC, MF and GJ provided the dataset used in the study. JL, CXC, JC, GJ and SF analysed and interpreted the data. JL drafted the manuscript. CXC, JC, EC, MF, GJ and SF proofed and revised the manuscript.

  • Funding Funding was provided in part by The Lowy Medical Research Institute, National Institutes of Health (P30 EY005722), and Unrestricted Grant from Research to Prevent Blindness.

  • Disclaimer The funding organizations had no role in the design or conduct of this research.

  • Competing interests Glenn J Jaffe (Consultant, Neurotech).

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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