PMID- 29655643
OWN - NLM
STAT- MEDLINE
DCOM- 20190627
LR  - 20191210
IS  - 1879-1891 (Electronic)
IS  - 0002-9394 (Linking)
VI  - 191
DP  - 2018 Jul
TI  - Automated Segmentation of Lesions Including Subretinal Hyperreflective Material 
      in Neovascular Age-related Macular Degeneration.
PG  - 64-75
LID - S0002-9394(18)30167-3 [pii]
LID - 10.1016/j.ajo.2018.04.007 [doi]
AB  - PURPOSE: To evaluate an automated segmentation algorithm with a convolutional 
      neural network (CNN) to quantify and detect intraretinal fluid (IRF), subretinal 
      fluid (SRF), pigment epithelial detachment (PED), and subretinal hyperreflective 
      material (SHRM) through analyses of spectral-domain optical coherence tomography 
      (SD-OCT) images from patients with neovascular age-related macular degeneration 
      (nAMD). DESIGN: Reliability and validity analysis of a diagnostic tool. METHODS: 
      We constructed a dataset including 930 B-scans from 93 eyes of 93 patients with 
      nAMD. A CNN-based deep neural network was trained using 11 550 augmented images 
      derived from 550 B-scans. The performance of the trained network was evaluated 
      using a validation set including 140 B-scans and a test set of 240 B-scans. The 
      Dice coefficient, positive predictive value (PPV), sensitivity, relative area 
      difference (RAD), and intraclass correlation coefficient (ICC) were used to 
      evaluate segmentation and detection performance. RESULTS: Good agreement was 
      observed for both segmentation and detection of lesions between the trained 
      network and clinicians. The Dice coefficients for segmentation of IRF, SRF, SHRM, 
      and PED were 0.78, 0.82, 0.75, and 0.80, respectively; the PPVs were 0.79, 0.80, 
      0.75, and 0.80, respectively; and the sensitivities were 0.77, 0.84, 0.73, and 
      0.81, respectively. The RADs were -4.32%, -10.29%, 4.13%, and 0.34%, 
      respectively, and the ICCs were 0.98, 0.98, 0.97, and 0.98, respectively. All 
      lesions were detected with high PPVs (range 0.94-0.99) and sensitivities (range 
      0.97-0.99). CONCLUSIONS: A CNN-based network provides clinicians with 
      quantitative data regarding nAMD through automatic segmentation and detection of 
      pathologic lesions, including IRF, SRF, PED, and SHRM.
CI  - Copyright (c) 2018 Elsevier Inc. All rights reserved.
FAU - Lee, Hyungwoo
AU  - Lee H
AD  - Department of Ophthalmology, Konkuk University Medical Center, Konkuk University 
      School of Medicine, Seoul, South Korea.
FAU - Kang, Kyung Eun
AU  - Kang KE
AD  - Department of Ophthalmology, Konkuk University Medical Center, Konkuk University 
      School of Medicine, Seoul, South Korea.
FAU - Chung, Hyewon
AU  - Chung H
AD  - Department of Ophthalmology, Konkuk University Medical Center, Konkuk University 
      School of Medicine, Seoul, South Korea.
FAU - Kim, Hyung Chan
AU  - Kim HC
AD  - Department of Ophthalmology, Konkuk University Medical Center, Konkuk University 
      School of Medicine, Seoul, South Korea. Electronic address: eyekim@kuh.ac.kr.
LA  - eng
PT  - Journal Article
DEP - 20180412
PL  - United States
TA  - Am J Ophthalmol
JT  - American journal of ophthalmology
JID - 0370500
SB  - IM
MH  - Aged
MH  - Aged, 80 and over
MH  - Female
MH  - Fluorescein Angiography/*methods
MH  - Follow-Up Studies
MH  - Fundus Oculi
MH  - Humans
MH  - Male
MH  - Middle Aged
MH  - Neural Networks, Computer
MH  - Reproducibility of Results
MH  - Retinal Pigment Epithelium/*pathology
MH  - Retrospective Studies
MH  - Subretinal Fluid/*diagnostic imaging
MH  - Tomography, Optical Coherence/*methods
MH  - Visual Acuity
MH  - Wet Macular Degeneration/*diagnosis
EDAT- 2018/04/16 06:00
MHDA- 2019/06/30 06:00
CRDT- 2018/04/16 06:00
PHST- 2018/01/10 00:00 [received]
PHST- 2018/03/31 00:00 [revised]
PHST- 2018/04/04 00:00 [accepted]
PHST- 2018/04/16 06:00 [pubmed]
PHST- 2019/06/30 06:00 [medline]
PHST- 2018/04/16 06:00 [entrez]
AID - S0002-9394(18)30167-3 [pii]
AID - 10.1016/j.ajo.2018.04.007 [doi]
PST - ppublish
SO  - Am J Ophthalmol. 2018 Jul;191:64-75. doi: 10.1016/j.ajo.2018.04.007. Epub 2018 
      Apr 12.