PMID- 34004000
OWN - NLM
STAT- MEDLINE
DCOM- 20210628
LR  - 20221119
IS  - 2164-2591 (Electronic)
IS  - 2164-2591 (Linking)
VI  - 10
IP  - 4
DP  - 2021 Apr 1
TI  - Automatic Segmentation in Multiple OCT Layers For Stargardt Disease 
      Characterization Via Deep Learning.
PG  - 24
LID - 10.1167/tvst.10.4.24 [doi]
LID - 24
AB  - PURPOSE: This study sought to perform automated segmentation of 11 retinal layers 
      and Stargardt-associated features on spectral-domain optical coherence tomography 
      (SD-OCT) images and to analyze differences between normal eyes and eyes diagnosed 
      with Stargardt disease. METHODS: Automated segmentation was accomplished through 
      application of the deep learning-shortest path (DL-SP) framework, a shortest path 
      segmentation approach that is enhanced by a deep learning fully convolutional 
      neural network. To compare normal eyes and eyes diagnosed with Stargardt disease, 
      various retinal layer thickness and intensity feature maps associated with the 
      outer retinal layers were generated. RESULTS: The automated DL-SP approach 
      achieved a mean difference within a subpixel accuracy range for all layers when 
      compared to manually traced layers by expert graders. The algorithm achieved mean 
      and absolute mean differences in border positions for Stargardt features of -0.11 
      +/- 4.17 pixels and 1.92 +/- 3.71 pixels, respectively. In several of the feature 
      maps generated, the characteristic Stargardt features of flecks and 
      atrophic-appearing lesions were readily visualized. CONCLUSIONS: To the best of 
      our knowledge, this is the first automated algorithm for 11 retinal layer 
      segmentation on OCT in eyes with Stargardt disease, and, furthermore, the feature 
      differences found between eyes diagnosed with Stargardt disease and normal eyes 
      may inform new insights and the better understanding of retinal characteristic 
      morphologic changes caused by Stargardt disease. TRANSLATIONAL RELEVANCE: The 
      automated algorithm's performance and the feature differences found using the 
      algorithm's segmentation support the future applications of SD-OCT for the 
      quantitative monitoring of Stargardt disease.
FAU - Mishra, Zubin
AU  - Mishra Z
AD  - Doheny Image Analysis Laboratory, Doheny Eye Institute, Los Angeles, CA, USA.
AD  - Case Western Reserve University School of Medicine, Cleveland, OH, USA.
FAU - Wang, Ziyuan
AU  - Wang Z
AD  - Doheny Image Analysis Laboratory, Doheny Eye Institute, Los Angeles, CA, USA.
AD  - The University of California, Los Angeles, CA, USA.
FAU - Sadda, SriniVas R
AU  - Sadda SR
AD  - Doheny Image Analysis Laboratory, Doheny Eye Institute, Los Angeles, CA, USA.
AD  - The University of California, Los Angeles, CA, USA.
FAU - Hu, Zhihong
AU  - Hu Z
AD  - Doheny Image Analysis Laboratory, Doheny Eye Institute, Los Angeles, CA, USA.
LA  - eng
GR  - R21 EY029839/EY/NEI NIH HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
PL  - United States
TA  - Transl Vis Sci Technol
JT  - Translational vision science & technology
JID - 101595919
SB  - IM
MH  - Algorithms
MH  - *Deep Learning
MH  - Humans
MH  - Retina/diagnostic imaging
MH  - Stargardt Disease
MH  - Tomography, Optical Coherence
PMC - PMC8083069
COIS- Disclosure: Z. Mishra, None; Z. Wang, None; S.R. Sadda, None; Z. Hu, None
EDAT- 2021/05/19 06:00
MHDA- 2021/06/29 06:00
PMCR- 2021/04/21
CRDT- 2021/05/18 17:23
PHST- 2021/05/18 17:23 [entrez]
PHST- 2021/05/19 06:00 [pubmed]
PHST- 2021/06/29 06:00 [medline]
PHST- 2021/04/21 00:00 [pmc-release]
AID - 2772502 [pii]
AID - TVST-20-3250 [pii]
AID - 10.1167/tvst.10.4.24 [doi]
PST - ppublish
SO  - Transl Vis Sci Technol. 2021 Apr 1;10(4):24. doi: 10.1167/tvst.10.4.24.