论文标题
Livelayer:一个半自动软件程序,用于分割层和糖尿病黄斑水肿在光学相干断层扫描图像中
Livelayer: A Semi-Automatic Software Program for Segmentation of Layers and Diabetic Macular Edema in Optical Coherence Tomography Images
论文作者
论文摘要
鉴于光学相干断层扫描(OCT)成像的能力表现出各种眼部疾病和神经系统疾病的症状,因此对OCT图像分割的需求和相应的数据解释的需求比以往任何时候都更加感受。在本文中,我们希望通过设计一个半自动软件程序,以应用8种不同黄斑层的可靠分割以及概述视网膜病理(例如糖尿病性黄斑水肿)来满足这种需求。该软件可容纳一种新型的基于图的半自动方法,称为“ Livelayer”,该方法专为视网膜层和流体的直接分割而设计。该方法主要基于Dijkstra的最短路径(SPF)算法和Live-Wire函数,以及对分段图像的一些预处理操作。该软件确实适用于获得层的详细分割,清晰或不清晰的流体对象的确切定位以及地面真相,与常见的手动分割方法相比,要求较少的努力。它作为计算变形OCT图像中不规则性指数的工具也很有价值。 ILM,IPL-INL,OPL-ONL分割所需的时间(秒)远小于手动分割的时间(最小值)和15.57S的时间(最大值)。半自动标记和黄金标准数据之间的未签名误差(像素)平均为ILM,IPL-INL,OPL-ONL分别为2.7、1.9、2.1。平淡的altman情节表明了Livelayer和手动算法之间的完美一致性,并且可以互换使用它们。重复性误差约为OPL-ONL的一个像素,而其他两个像素<1。比较两种算法和在流体对象分割时获得可重复性的骰子得分是在可接受的水平上。
Given the capacity of Optical Coherence Tomography (OCT) imaging to display symptoms of a wide variety of eye diseases and neurological disorders, the need for OCT image segmentation and the corresponding data interpretation is latterly felt more than ever before. In this paper, we wish to address this need by designing a semi-automatic software program for applying reliable segmentation of 8 different macular layers as well as outlining retinal pathologies such as diabetic macular edema. The software accommodates a novel graph-based semi-automatic method, called ''Livelayer'' which is designed for straightforward segmentation of retinal layers and fluids. This method is chiefly based on Dijkstra's Shortest Path (SPF) algorithm and the Live-wire function together with some preprocessing operations on the to-be-segmented images. The software is indeed suitable for obtaining detailed segmentation of layers, exact localization of clear or unclear fluid objects and the ground truth, demanding far less endeavor in comparison to a common manual segmentation method. It is also valuable as a tool for calculating the irregularity index in deformed OCT images. The amount of time (seconds) that Livelayer required for segmentation of ILM, IPL-INL, OPL-ONL was much less than that for the manual segmentation, 5s for the ILM (minimum) and 15.57s for the OPL-ONL (maximum). The unsigned errors (pixels) between the semi-automatically labeled and gold standard data was on average 2.7, 1.9, 2.1 for ILM, IPL-INL, OPL-ONL, respectively. The Bland-Altman plots indicated perfect concordance between the Livelayer and the manual algorithm and that they could be used interchangeably. The repeatability error was around one pixel for the OPL-ONL and < 1 for the other two. The dice scores for comparing the two algorithms and for obtaining the repeatability on segmentation of fluid objects were at acceptable levels.