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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
1. Introduction
The reported errors in [1] were mistakenly computed, due to an an error in conversion between file formats. We include corrected versions of Tables 1 and 2.
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.
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.
2. Discussion and conclusion
Our method (MGDM) still significantly outperforms RF+Graph in six of the nine boundaries and has better overall accuracy. The errors reported for both methods are lower than those reported in [1] and the mean boundary errors reported for the RF+Graph method are now consistent with those reported in [2]. Note that the within subject variation was not included in our computation of the standard deviation, which is why our standard deviations are significantly lower than those reported in [2].
Acknowledgments
This work was supported by the NIH/NEI R21-EY022150 and the NIH/NINDS R01-NS082347.
References and links
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]