June 2021
Volume 62, Issue 8
Open Access
ARVO Annual Meeting Abstract  |   June 2021
A Deep-Learning Based Algorithm for Automated Segmentation of Geographic Atrophy in Swept-Source Optical Coherence Tomography
Author Affiliations & Notes
  • Varsha Pramil
    New England Eye Center, Boston, Massachusetts, United States
    Tufts University School of Medicine, Boston, Massachusetts, United States
  • Luis De Sisternes
    Carl Zeiss Meditec Inc, Dublin, California, United States
  • Lars Omlor
    Carl Zeiss Meditec Inc, Pleasanton, California, United States
  • Warren Lewis
    Bayside Photonics, Inc., Ohio, United States
  • Sophie Kubach
    Carl Zeiss Meditec Inc, Dublin, California, United States
  • Harris Asad Sheikh
    New England Eye Center, Boston, Massachusetts, United States
    University of Massachusetts System, Boston, Massachusetts, United States
  • Mary K Durbin
    Carl Zeiss Meditec Inc, Dublin, California, United States
  • Nadia K Waheed
    New England Eye Center, Boston, Massachusetts, United States
  • Footnotes
    Commercial Relationships   Varsha Pramil, None; Luis De Sisternes, Carl Zeiss Meditec (E); Lars Omlor, Carl Zeiss Meditec (E); Warren Lewis, Bayside Photonics, Inc. (E), Carl Zeiss Meditec (C); Sophie Kubach, Carl Zeiss Meditec (E); Harris Asad Sheikh, None; Mary Durbin, Carl Zeiss Meditec (E); Nadia Waheed, Allegro (C), Apellis (C), Astellas (C), Boehringer Ingelheim (C), Genentech (C), Gyroscope (E), Heidelberg (R), Nidek (R), Nidek (C), Ocudyne (I), Optovue (R), Regeneron (C), Stealth (C)
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science June 2021, Vol.62, 117. doi:
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    • Get Citation

      Varsha Pramil, Luis De Sisternes, Lars Omlor, Warren Lewis, Sophie Kubach, Harris Asad Sheikh, Mary K Durbin, Nadia K Waheed; A Deep-Learning Based Algorithm for Automated Segmentation of Geographic Atrophy in Swept-Source Optical Coherence Tomography. Invest. Ophthalmol. Vis. Sci. 2021;62(8):117.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : This work presents the first deep-learning based GA segmentation algorithm for swept-source OCT that provides accurate and reproducible results in GA assessment with high sensitivity to GA changes over time.

Methods : An automated algorithm was trained with 6x6 mm macular SS-OCT scans from 58 GA eyes (38/20 split for training/validation set). It utilizes scan volume data to generate three image inputs characterizing the main OCT features of GA: hyper-illumination in sub RPE slab, regions of RPE loss, and loss of retinal thickness (Figure 1). Advanced data augmentation techniques helped compensate for the small training data size. To evaluate the accuracy of the GA segmentations, 180 SS-OCT macular scans from 30 GA eyes collected from 3 different sites were considered: each eye had 3 repeated scans at baseline and follow-up visits (average of 16.74 months). Area measurements were corrected by their square-root and used to compute enlargement rate per year between the visits. The GA delineations, area measurements and enlargement rates generated by the automated algorithm in addition to algorithm repeatability in repeated scans were compared to the ground-truth manual delineations annotated by two graders.

Results : Figure 2 summarizes the performance of the algorithm. Automated GA delineation accuracy between the algorithm and two graders was measured using the Dice coefficient with average values of 0.88 and 0.87, respectively. GA area measurements produced by the algorithm were comparable and showed no significant difference to those from each grader, with an average absolute difference of 0.19 (p-value 0.73) and 0.19 (p-value 0.77), respectively. GA enlargement rate computed by the algorithm also showed no significant differences to the two graders, with differences of 0.13 (p-value 0.26) and 0.12 (p-value 0.71), respectively. The algorithm also presented a high repeatability for area and enlargement rate measurements, with intra-class repeatability coefficients of 0.997 and 0.947, respectively.

Conclusions : Despite a small training data size, this deep learning based automated GA segmentation algorithm is able to produce accurate and reproducible results in GA assessment with high sensitivity for GA changes over time.

This is a 2021 ARVO Annual Meeting abstract.

 

Figure 1. Input, processing and output of the proposed algorithm.

Figure 1. Input, processing and output of the proposed algorithm.

 

Figure 2. Performance of proposed algorithm.

Figure 2. Performance of proposed algorithm.

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