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

TL;DR

This paper introduces novel data augmentation techniques, FDDA and PRLC, enabling retinal layer segmentation in OCT images without the need for prior flattening, thus handling structural variations due to ophthalmopathy or myopia.

Contribution

The paper presents two innovative augmentation methods, FDDA and PRLC, that improve retinal layer segmentation by accommodating structural variations without flattening.

Findings

FDDA and PRLC improve segmentation accuracy

Methods work on multiple datasets including OCT MS and Duke Cyst DME

Segmentation achieved without retinal flattening

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.