Formula-Driven Data Augmentation and Partial Retinal Layer Copying for Retinal Layer Segmentation
Not on the map. Why?
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 11 of its 25 references that are in the map. First posted on arXiv 2 Oct 2024.
Builds on
11 of 25 references.
References · 11
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
- 2019CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Featurescited by 2,875
- 2017Improved Regularization of Convolutional Neural Networks with Cutoutcited by 1,623
- 2017Random Erasing Data Augmentationcited by 1,235
- 2017ReLayNet: Retinal Layer and Fluid Segmentation of Macular Optical Coherence Tomography using Fully Convolutional Networkcited by 37
- 2020Pathological Retinal Region Segmentation From OCT Images Using Geometric Relation Based Augmentationcited by 14
- 2021Multi-scale GCN-assisted two-stage network for joint segmentation of retinal layers and disc in peripapillary OCT imagescited by 14
- 2022SD-LayerNet: Semi-supervised retinal layer segmentation in OCT using disentangled representation with anatomical priorscited by 7
- 2023Simultaneous Alignment and Surface Regression Using Hybrid 2D-3D Networks for 3D Coherent Layer Segmentation of Retinal OCT Images with Full and Sparse Annotationscited by 7
- 2022Simultaneous Alignment and Surface Regression Using Hybrid 2D-3D Networks for 3D Coherent Layer Segmentation of Retina OCT Imagescited by 3
- 2022A deep learning network with differentiable dynamic programming for retina OCT surface segmentationcited by 2
Built on by
Citing papers per year: 2026: 1.
1 paper.