PMID- 21911579
OWN - NLM
STAT- MEDLINE
DCOM- 20111213
LR  - 20220310
IS  - 1552-5783 (Electronic)
IS  - 0146-0404 (Print)
IS  - 0146-0404 (Linking)
VI  - 52
IP  - 11
DP  - 2011 Oct 21
TI  - Computerized macular pathology diagnosis in spectral domain optical coherence 
      tomography scans based on multiscale texture and shape features.
PG  - 8316-22
LID - 10.1167/iovs.10-7012 [doi]
AB  - PURPOSE: To develop an automated method to identify the normal macula and three 
      macular pathologies (macular hole [MH], macular edema [ME], and age-related 
      macular degeneration [AMD]) from the fovea-centered cross sections in 
      three-dimensional (3D) spectral-domain optical coherence tomography (SD-OCT) 
      images. METHODS: A sample of SD-OCT macular scans (macular cube 200 x 200 or 512 
      x 128 scan protocol; Cirrus HD-OCT; Carl Zeiss Meditec, Inc., Dublin, CA) was 
      obtained from healthy subjects and subjects with MH, ME, and/or AMD (dataset for 
      development: 326 scans from 136 subjects [193 eyes], and dataset for testing: 131 
      scans from 37 subjects [58 eyes]). A fovea-centered cross-sectional slice for 
      each of the SD-OCT images was encoded using spatially distributed multiscale 
      texture and shape features. Three ophthalmologists labeled each fovea-centered 
      slice independently, and the majority opinion for each pathology was used as the 
      ground truth. Machine learning algorithms were used to identify the 
      discriminative features automatically. Two-class support vector machine 
      classifiers were trained to identify the presence of normal macula and each of 
      the three pathologies separately. The area under the receiver operating 
      characteristic curve (AUC) was calculated to assess the performance. RESULTS: The 
      cross-validation AUC result on the development dataset was 0.976, 0.931, 0939, 
      and 0.938, and the AUC result on the holdout testing set was 0.978, 0.969, 0.941, 
      and 0.975, for identifying normal macula, MH, ME, and AMD, respectively. 
      CONCLUSIONS: The proposed automated data-driven method successfully identified 
      various macular pathologies (all AUC > 0.94). This method may effectively 
      identify the discriminative features without relying on a potentially error-prone 
      segmentation module.
FAU - Liu, Yu-Ying
AU  - Liu YY
AD  - School of Interactive Computing, Georgia Institute of Technology, Atlanta, 
      Georgia, USA.
FAU - Ishikawa, Hiroshi
AU  - Ishikawa H
FAU - Chen, Mei
AU  - Chen M
FAU - Wollstein, Gadi
AU  - Wollstein G
FAU - Duker, Jay S
AU  - Duker JS
FAU - Fujimoto, James G
AU  - Fujimoto JG
FAU - Schuman, Joel S
AU  - Schuman JS
FAU - Rehg, James M
AU  - Rehg JM
LA  - eng
GR  - R01-EY011289/EY/NEI NIH HHS/United States
GR  - R01-EY013178/EY/NEI NIH HHS/United States
GR  - R01 EY013178/EY/NEI NIH HHS/United States
GR  - P30-EY008098/EY/NEI NIH HHS/United States
GR  - R01 EY011289/EY/NEI NIH HHS/United States
GR  - P30 EY008098/EY/NEI NIH HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
PT  - Research Support, Non-U.S. Gov't
DEP - 20111021
PL  - United States
TA  - Invest Ophthalmol Vis Sci
JT  - Investigative ophthalmology & visual science
JID - 7703701
SB  - IM
MH  - *Artificial Intelligence
MH  - Databases, Factual
MH  - Diagnosis, Computer-Assisted/*methods/standards/statistics & numerical data
MH  - Humans
MH  - Macula Lutea/*pathology
MH  - Macular Degeneration/pathology
MH  - Macular Edema/pathology
MH  - Observer Variation
MH  - ROC Curve
MH  - Reproducibility of Results
MH  - Retinal Diseases/*pathology
MH  - Retinal Perforations/pathology
MH  - Tomography, Optical Coherence/*methods/standards/statistics & numerical data
PMC - PMC3208114
EDAT- 2011/09/14 06:00
MHDA- 2011/12/14 06:00
PMCR- 2012/04/01
CRDT- 2011/09/14 06:00
PHST- 2011/09/14 06:00 [entrez]
PHST- 2011/09/14 06:00 [pubmed]
PHST- 2011/12/14 06:00 [medline]
PHST- 2012/04/01 00:00 [pmc-release]
AID - iovs.10-7012 [pii]
AID - 10-7012 [pii]
AID - 10.1167/iovs.10-7012 [doi]
PST - epublish
SO  - Invest Ophthalmol Vis Sci. 2011 Oct 21;52(11):8316-22. doi: 10.1167/iovs.10-7012.