提出两种新数据增强方法，让模型无需扁平化也能准确分割视网膜层。
Formula-Driven Data Augmentation and Partial Retinal Layer Copying for Retinal Layer Segmentation
- 用数学公式垂直移动OCT图像每列，模拟不同视网膜结构。
- 将视网膜层部分复制粘贴到外侧区域，增加样本多样性。
- 适用于有屈光不正或眼病导致结构变形的复杂病例。
基于OCT图像的主流视网膜层分割方法通常假设视网膜已预先扁平化，因此难以处理因眼病或近视导致的结构变化或曲率。为提高实际应用性，本文提出两种新颖的数据增强方法。公式驱动的数据增强（FDDA）通过给定数学公式垂直移动OCT图像每一列，模拟多种视网膜结构。同时提出部分视网膜层复制（PRLC），将视网膜层的一部分复制并粘贴至视网膜外部区域。在OCT MS与健康对照数据集及杜克囊肿型DME数据集上的实验表明，使用FDDA与PRLC后，即使原本假设视网膜扁平的方法，也能在不进行扁平化的情况下准确检测视网膜层边界。
原文摘要 · Abstract (English)
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.
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