June 2022
Volume 63, Issue 7
Open Access
ARVO Annual Meeting Abstract  |   June 2022
Automatic segmentation of geographic atrophy in OCT scans using deep learning
Author Affiliations & Notes
  • Yuxuan Cheng
    Bioengineering, University of Washington, Seattle, Washington, United States
  • Zhongdi Chu
    Bioengineering, University of Washington, Seattle, Washington, United States
  • Mengxi Shen
    Ophthalmology, University of Miami Health System Bascom Palmer Eye Institute, Miami, Florida, United States
  • Rita Laiginhas
    Ophthalmology, University of Miami Health System Bascom Palmer Eye Institute, Miami, Florida, United States
  • Jeremy Liu
    Ophthalmology, University of Miami Health System Bascom Palmer Eye Institute, Miami, Florida, United States
  • Yingying Shi
    Ophthalmology, University of Miami Health System Bascom Palmer Eye Institute, Miami, Florida, United States
  • Jianqing Li
    Ophthalmology, University of Miami Health System Bascom Palmer Eye Institute, Miami, Florida, United States
  • Hao Zhou
    Bioengineering, University of Washington, Seattle, Washington, United States
  • Qinqin Zhang
    Bioengineering, University of Washington, Seattle, Washington, United States
  • Giovanni Gregori
    Ophthalmology, University of Miami Health System Bascom Palmer Eye Institute, Miami, Florida, United States
  • Philip J Rosenfeld
    Ophthalmology, University of Miami Health System Bascom Palmer Eye Institute, Miami, Florida, United States
  • Ruikang K Wang
    Bioengineering, University of Washington, Seattle, Washington, United States
    Ophthalmology, University of Washington, Seattle, Washington, United States
  • Footnotes
    Commercial Relationships   Yuxuan Cheng None; Zhongdi Chu Verana Health, Code E (Employment); Mengxi Shen None; Rita Laiginhas None; Jeremy Liu None; Yingying Shi None; Jianqing Li None; Hao Zhou None; Qinqin Zhang None; Giovanni Gregori Carl Zeiss Meditec, Code F (Financial Support), Carl Zeiss Meditec, Code P (Patent); Philip Rosenfeld Carl Zeiss Meditec, Code C (Consultant/Contractor), Carl Zeiss Meditec, Code F (Financial Support); Ruikang Wang Carl Zeiss Meditec, Code C (Consultant/Contractor), Carl Zeiss Meditec, Code F (Financial Support), Carl Zeiss Meditec, Code P (Patent)
  • Footnotes
    Support  NIH Grant EY28753, EY01481, Carl Zeiss Meditec. Research to Prevent Blindness
Investigative Ophthalmology & Visual Science June 2022, Vol.63, 1040 – F0287. doi:
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    • Get Citation

      Yuxuan Cheng, Zhongdi Chu, Mengxi Shen, Rita Laiginhas, Jeremy Liu, Yingying Shi, Jianqing Li, Hao Zhou, Qinqin Zhang, Giovanni Gregori, Philip J Rosenfeld, Ruikang K Wang; Automatic segmentation of geographic atrophy in OCT scans using deep learning. Invest. Ophthalmol. Vis. Sci. 2022;63(7):1040 – F0287.

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

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Abstract

Purpose : To automatically identify, segment, and quantify geographic atrophy (GA) based on optical attenuation coefficients (OACs) and optical coherence tomography (OCT) datasets using deep learning algorithms.

Methods : Normal eyes and eyes with GA secondary to age-related macular degeneration (AMD) were imaged with swept-source OCT using a 6x6 mm scan pattern. Depth-resolved OACs were calculated from OCT scans. For each OCT scan, three images were generated and combined to produce pseudo-color images (Figure 1): (1) the OAC-identified RPE elevation from Bruch’s membrane (BM), (2) the sum OAC projection between the inner limiting membrane and BM, and (3) the sub-retinal pigment epithelium slab projection (subRPE) extending from 64 to 400µm below BM. An attention improved U-net model was trained to segment the composite images with a focal loss for a better classification of evolving GA lesions. All GA lesions were manually labeled from the subRPE slabs by senior graders for evaluating the model. User-friendly software with the model was developed to test the algorithm in clinical settings. The performance of the model was evaluated using DICE similarity coefficients (DSCs). The areas of the GA lesions were calculated and compared with manual segmentations using Pearson’s correlation and Bland-Altman analyses. Both the model output and the manual outlines excluded GA lesions with greatest linear dimension less than 250µm.

Results : A set of 153 GA eyes, 30 drusen only eyes, and 60 normal eyes were used to develop and test the model. The dataset was split 80:20 for training:validation. Another 30 AMD eyes with GA lesions were prepared for testing. The model reached the dice coefficients of 0.958, 0.941, 0.930 on the training, validation, and testing after 300 epochs of training, respectively. The mean area difference on the testing set was 0.106 mm2 between manual segmentation and the model, and the Pearson’s correlation was 0.957. Figure 2 shows a working example of the software on a case with GA lesions.

Conclusions : The proposed model using composite color images derived from OCT scans effectively and accurately identified, segmented, and quantified GA lesions.

This abstract was presented at the 2022 ARVO Annual Meeting, held in Denver, CO, May 1-4, 2022, and virtually.

 

 

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