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Updated: Dec 28, 2025
Automated 3D Optical Coherence Tomography to Elucidate Biofilm Morphogenesis Over Large Spatial Scales
Published on: August 21, 2019
Impact of manual correction over automated segmentation of spectral domain optical coherence tomography
Alexandre Gomes Bortoloti de Azevedo1, Guilherme Eiichi da Silva Takitani1, Bruno Rebello Godoy1
1Departament of Ophthalmology and Visual Sciences, Federal University of São Paulo-UNIFESP/EPM, Rua Pedro de Toledo, 781, São Paulo, SP 04039-032 Brazil.
Objective:
To study the automated segmentation of retinal layers using spectral domain optical coherence tomography (OCT) and the impact of manual correction over segmentation mistakes.
Methods:
This was a retrospective, cross-sectional, comparative study that compared the automated segmentation of macular thickness using Spectralis™ OCT technology (Heidelberg Engineering, Heidelberg, Germany) versus manual segmentation in eyes with no macular changes, macular cystoid edema (CME), and choroidal neovascularization (CNV). Automated segmentation of macular thickness was manually corrected by two independent examiners and reanalyzed by them together in case of disagreement.
Results:
In total, 306 eyes of 254 consecutive patients were evaluated. No statistically significant differences were noted between automated and manual macular thickness measurements in patients with normal maculas, while a statistically significant difference was found in central thickness in patients with CNV and with CME. Segmentation mistakes in macular OCTs were present in 5.3% (5 of 95) in the normal macula group, 16.4% (23 of 140) in the CME group, and 66.2% (47 of 71) in CNV group. The difference between automated and manual macular thickness was higher than 10% in 1.4% (2 of 140) in the CME group and in 28.17% (20 of 71) in the CNV group. Only one case in the normal group had a higher than 10% segmentation error (1 of 95).
Conclusion:
The evaluation of automated segmented OCT images revealed appropriate delimitation of macular thickness in patients with no macular changes or with CME, since the frequency and magnitude of the segmentation mistakes had low impact over clinical evaluation of the images. Conversely, automated macular thickness segmentation in patients with CNV showed a high frequency and magnitude of mistakes, with potential impact on clinical analysis.
