Abstract

Boundary errors were incorrectly computed in our paper [Biomed. Opt. Express 5(4), 1063 (2014)], which resulted from the manual segmentations being incorrectly converted between file formats. In particular, our paper mischaracterized the error of the method in comparison to that of Lang et al. [Biomed. Opt. Express 4(7), 1133–1152 (2013)]. We include corrected tables, replacing Tables 1 and 2 in [Biomed. Opt. Express 5(4), 1063 (2014)].

© 2015 Optical Society of America

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References

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  1. A. Carass, A. Lang, M. Hauser, P. A. Calabresi, H. S. Ying, and J. L. Prince, “Multiple-object geometric deformable model for segmentation of macular OCT,” Biomed. Opt. Express 5, 1062–1074 (2014).
    [Crossref] [PubMed]
  2. A. Lang, A. Carass, M. Hauser, E. S. Sotirchos, P. A. Calabresi, H. S. Ying, and J. L. Prince, “Retinal layer segmentation of macular OCT images using boundary classification,” Biomed. Opt. Express 4, 1133–1152 (2013).
    [Crossref] [PubMed]

2014 (1)

2013 (1)

Calabresi, P. A.

Carass, A.

Hauser, M.

Lang, A.

Prince, J. L.

Sotirchos, E. S.

Ying, H. S.

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Tables (2)

Tables Icon

Table 1 Mean (and standard deviations) of the Dice Coefficient across the eight retinal layers. A paired Wilcoxon rank sum test was used to test the significance of any improvement between RF+Graph [2] and our method, with strong significance (an α level of 0.001) in two of the eight layers. However, RF+Graph was also significantly better than MGDM on two of the eight layers. In three of the four remaining layers, MGDM is marginally better than RF+Graph without reaching statistical significance.

Tables Icon

Table 2 Mean absolute errors (and standard deviation) in microns for our method (MGDM) in comparison to RF+Graph [2] on the nine estimated boundaries. A paired Wilcoxon rank sum test was used to compute p-values between the two methods with strong significance (an α level of 0.001) in six of the nine boundaries. However RF+Graph was significantly better than MGDM on one of the nine boundaries.

Metrics