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Formula-Driven Data Augmentation and Partial Retinal Layer Copying for Retinal Layer Segmentation

Tsubasa Konno, Takahiro Ninomiya, Kanta Miura, Koichi Ito, Noriko Himori, Parmanand Sharma, Toru Nakazawa, Takafumi Aoki

TL;DR

It is demonstrated that the use of FDDA and PRLC makes it possible to detect the boundaries of retinal layers without flattening even retinal layer segmentation methods that assume flattening of the retina.

Abstract

Major retinal layer segmentation methods from OCT images assume that the retina is flattened in advance, and thus cannot always deal with retinas that have changes in retinal structure due to ophthalmopathy and/or curvature due to myopia. To eliminate the use of flattening in retinal layer segmentation for practicality of such methods, we propose novel data augmentation methods for OCT images. Formula-driven data augmentation (FDDA) emulates a variety of retinal structures by vertically shifting each column of the OCT images according to a given mathematical formula. We also propose partial retinal layer copying (PRLC) that copies a part of the retinal layers and pastes it into a region outside the retinal layers. Through experiments using the OCT MS and Healthy Control dataset and the Duke Cyst DME dataset, we demonstrate that the use of FDDA and PRLC makes it possible to detect the boundaries of retinal layers without flattening even retinal layer segmentation methods that assume flattening of the retina.

Formula-Driven Data Augmentation and Partial Retinal Layer Copying for Retinal Layer Segmentation

TL;DR

It is demonstrated that the use of FDDA and PRLC makes it possible to detect the boundaries of retinal layers without flattening even retinal layer segmentation methods that assume flattening of the retina.

Abstract

Major retinal layer segmentation methods from OCT images assume that the retina is flattened in advance, and thus cannot always deal with retinas that have changes in retinal structure due to ophthalmopathy and/or curvature due to myopia. To eliminate the use of flattening in retinal layer segmentation for practicality of such methods, we propose novel data augmentation methods for OCT images. Formula-driven data augmentation (FDDA) emulates a variety of retinal structures by vertically shifting each column of the OCT images according to a given mathematical formula. We also propose partial retinal layer copying (PRLC) that copies a part of the retinal layers and pastes it into a region outside the retinal layers. Through experiments using the OCT MS and Healthy Control dataset and the Duke Cyst DME dataset, we demonstrate that the use of FDDA and PRLC makes it possible to detect the boundaries of retinal layers without flattening even retinal layer segmentation methods that assume flattening of the retina.
Paper Structure (10 sections, 3 equations, 5 figures, 1 table)

This paper contains 10 sections, 3 equations, 5 figures, 1 table.

Table of Contents

  1. Introduction
  2. Method
  3. Formula-Driven Data Augmentation (FDDA)
  4. Partial Retinal Layer Copying (PRLC)
  5. Experiments
  6. Datasets
  7. Experimental Condition
  8. Experimental Results and Discussion
  9. Conclusion
  10. Disclosure of Interests.

Figures (5)

  • Figure 1: OCT images in the World's Largest Online Annotated SD-OCT dataset AMD before and after flattening, where the yellow, green, and blue lines indicate the inner limiting membrane (ILM), the inner aspect of the retinal pigment epithelium drusen complex (IRPE), and the outer aspect of Bruch's membrane (OBM), respectively: (a) and (b) are the healthy subjects and (c) and (d) are the age-related macular degeneration (AMD) patients.
  • Figure 2: Overview of FDDA using only the first-order shift $\Delta_1(n_2)$ with $a(1)=1$.
  • Figure 3: Examples of applying FDDA to OCT images in MSHC, where each of the zero-order, first-order, and second-order shifts is applied to the input image for simplicity: (a) the zero-order shift $\Delta_0(n_2)$, (b) the first-order shift $\Delta_1(n_2)$, (c) the second-order shift $\Delta_2(n_2)$, and (d) the combined shift $\Delta(n_2)$, and an example of applying RandomAffine for comparison. Colored lines on each image indicate the annotated boundaries between the retinal layers.
  • Figure 4: Examples of applying PRLC to OCT images in MSHC, where the red dashed box indicates the pasted retinal layer area. An example of applying CutMix is also shown for comparison. Colored lines on each image indicate the annotated boundaries between the retinal layers.
  • Figure 5: Detection results of the retinal layer boundaries from the OCT images of MSHC using each method. The red dotted circles indicate the region with large errors.