June 2020
Volume 61, Issue 7
Free
ARVO Annual Meeting Abstract  |   June 2020
Multiple pathology detection in macular OCT B-scans using localized-3D information
Author Affiliations & Notes
  • Niranchana Manivannan
    Carl Zeiss Meditec, Inc., Dublin, California, United States
  • Gary C Lee
    Carl Zeiss Meditec, Inc., Dublin, California, United States
  • Hugang Ren
    Carl Zeiss Meditec, Inc., Dublin, California, United States
  • Sophia Yu
    Carl Zeiss Meditec, Inc., Dublin, California, United States
  • Patricia Sha
    Carl Zeiss Meditec, Inc., Dublin, California, United States
  • Krunalkumar Ramanbhai Patel
    Carl Zeiss India (Bangalore) Pvt. Ltd, ZEISS GROUP, Bangalore, Karnataka, India
  • SANDIPAN CHAKROBORTY
    Carl Zeiss India (Bangalore) Pvt. Ltd, ZEISS GROUP, Bangalore, Karnataka, India
  • Rishi Singh
    Center for Ophthalmic Bioinformatics, Cole Eye Institute, Cleveland Clinic, Cleveland, Ohio, United States
  • Katherine Talcott
    Center for Ophthalmic Bioinformatics, Cole Eye Institute, Cleveland Clinic, Cleveland, Ohio, United States
  • Alline Melo
    Center for Ophthalmic Bioinformatics, Cole Eye Institute, Cleveland Clinic, Cleveland, Ohio, United States
  • Thais Conti
    Center for Ophthalmic Bioinformatics, Cole Eye Institute, Cleveland Clinic, Cleveland, Ohio, United States
  • Tyler E Greenlee
    Center for Ophthalmic Bioinformatics, Cole Eye Institute, Cleveland Clinic, Cleveland, Ohio, United States
  • Eric Chen
    Case Western Reserve University School of Medicine, Cleveland, California, United States
  • Grant L Hom
    Center for Ophthalmic Bioinformatics, Cole Eye Institute, Cleveland Clinic, Cleveland, Ohio, United States
  • Neil D'Souza
    Carl Zeiss Meditec, Inc., Dublin, California, United States
  • Mary K Durbin
    Carl Zeiss Meditec, Inc., Dublin, California, United States
  • Footnotes
    Commercial Relationships   Niranchana Manivannan, Carl Zeiss Meditec, Inc. (E); Gary Lee, Carl Zeiss Meditec, Inc. (E); Hugang Ren, Carl Zeiss Meditec, Inc. (E); Sophia Yu, Carl Zeiss Meditec, Inc. (E); Patricia Sha, Carl Zeiss Meditec, Inc. (E); Krunalkumar Ramanbhai Patel, Carl Zeiss India (Bangalore) Pvt. Ltd (E); SANDIPAN CHAKROBORTY, Carl Zeiss India (Bangalore) Pvt. Ltd (E); Rishi Singh, Alcon (C), Apellis (F), Carl Zeiss Meditec, Inc. (C), Genentech (C), Graybug (F), Novartis (C), Regeneron (C); Katherine Talcott, Carl Zeiss Meditec, Inc. (F); Alline Melo, Carl Zeiss Meditec, Inc. (F); Thais Conti, Carl Zeiss Meditec, Inc. (F); Tyler Greenlee, Carl Zeiss Meditec, Inc. (F); Eric Chen, Carl Zeiss Meditec, Inc. (F); Grant Hom, Carl Zeiss Meditec, Inc. (F); Neil D'Souza, Carl Zeiss Meditec, Inc. (E); Mary Durbin, Carl Zeiss Meditec, Inc. (E)
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science June 2020, Vol.61, 3238. doi:
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    • Get Citation

      Niranchana Manivannan, Gary C Lee, Hugang Ren, Sophia Yu, Patricia Sha, Krunalkumar Ramanbhai Patel, SANDIPAN CHAKROBORTY, Rishi Singh, Katherine Talcott, Alline Melo, Thais Conti, Tyler E Greenlee, Eric Chen, Grant L Hom, Neil D'Souza, Mary K Durbin; Multiple pathology detection in macular OCT B-scans using localized-3D information. Invest. Ophthalmol. Vis. Sci. 2020;61(7):3238.

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

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Abstract

Purpose : The advent of data-intensive imaging technologies, along with the increasing number of patients, creates a need for an automatic triaging tool for improving workflow efficiency. In this study, we developed a multiple-pathology detector that can be used as a clinical decision support tool for macular OCT cubes.

Methods : This retrospective study used 76,544 B-scans from macular cubes (512x128) of 598 subjects (250 normals, 348 with ocular pathologies) acquired using CIRRUS™ HD-OCT 5000 (ZEISS, Dublin, CA). Each B-scan is labeled by the categories listed in figure 1 or as ungradable by two retina specialists. If there were a difference in the cube level grading, the ground truth was finalized after an adjudication by a third specialist and the B-scan level labels were created by combining the gradings of both specialists using ‘OR’ logic.

A 5-fold cross-validation with 80%-20% split was used. The training set was augmented to create a data set of 183,706 B-scans. A 3-channel ResNet-50 with Sigmoid activation adapted for multi-class multi-label detection (8 pathologies and 1 normal) was pre-trained on ImageNet images and was transfer trained with B-scans (224 x 224). In model 1, each B-scan is replicated to form 3-channel image. In model 2, each B-scan is combined with its two adjacent scans (above and below the corresponding B-scan) to create a localized-3D 3-channel image. In the 5-fold validation, if the probability of normal class was greater than 0.5, the B-scan was labeled as normal. If not, the B-scan was labeled as one or more of the pathologies.

Results : Figure 1 shows the average accuracy scores from 5-fold validation for both the models and the accuracies of all the labels increased in model 2 with the addition of localized 3D information. Figure 2 shows some of the examples where the proposed localized-3D information in model 2 improved the accuracy of the deduction.

Conclusions : The proposed model using localized-3D information performs better than the model using single B-scan. In this study, we developed a deep learning algorithm for multiple pathology detection that has the potential to function as a clinical decision support tool to improve the workflow efficiency.

This is a 2020 ARVO Annual Meeting abstract.

 

Figure1. Average accuracy scores from 5-fold validation

Figure1. Average accuracy scores from 5-fold validation

 

Figure 2. (a) False positives and (b) false negatives in model 1 which were classified correctly in model 2; (c) false positives and (d) false negatives in both model 1 and model 2

Figure 2. (a) False positives and (b) false negatives in model 1 which were classified correctly in model 2; (c) false positives and (d) false negatives in both model 1 and model 2

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