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

arXiv: https://arxiv.org/abs/2410.01185 · HTML: https://mlsanity.com/paper/2410.01185/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 25 references that are in the map.
- First posted on arXiv 2 Oct 2024.

## Builds on (most cited first, 11 of 11)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019)
- [Improved Regularization of Convolutional Neural Networks with Cutout](https://mlsanity.com/paper/1708.04552/) (2017)
- [Random Erasing Data Augmentation](https://mlsanity.com/paper/1708.04896/) (2017)
- [ReLayNet: Retinal Layer and Fluid Segmentation of Macular Optical Coherence Tomography using Fully Convolutional Network](https://mlsanity.com/paper/1704.02161/) (2017)
- [Pathological Retinal Region Segmentation From OCT Images Using Geometric Relation Based Augmentation](https://mlsanity.com/paper/2003.14119/) (2020)
- [Multi-scale GCN-assisted two-stage network for joint segmentation of retinal layers and disc in peripapillary OCT images](https://mlsanity.com/paper/2102.04799/) (2021)
- [SD-LayerNet: Semi-supervised retinal layer segmentation in OCT using disentangled representation with anatomical priors](https://mlsanity.com/paper/2207.00458/) (2022)
- [Simultaneous Alignment and Surface Regression Using Hybrid 2D-3D Networks for 3D Coherent Layer Segmentation of Retinal OCT Images with Full and Sparse Annotations](https://mlsanity.com/paper/2312.01726/) (2023)
- [Simultaneous Alignment and Surface Regression Using Hybrid 2D-3D Networks for 3D Coherent Layer Segmentation of Retina OCT Images](https://mlsanity.com/paper/2203.02390/) (2022)
- [A deep learning network with differentiable dynamic programming for retina OCT surface segmentation](https://mlsanity.com/paper/2210.06335/) (2022)

## Built on by (most cited first, 1 of 1)

- [PC-Seg: Progressive Cross-View Consistency for 3D OCT Segmentation from Sparse 2D Annotations](https://mlsanity.com/paper/2607.17718/) (2026)

## Abstract (arXiv)

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.