Retina layer segmentation using kernel graph cuts and continuous max-flow
Retina layer segmentation using kernel graph cuts and continuous max-flow
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DOI：
10.1364/oe.23.007366
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发表时间：
2015-03-23
期刊：
影响因子：
3.8
通讯作者：
Li, Y.
中科院分区：
文献类型：
--
作者：
Kaba, D.;Wang, Y.;Li, Y.
圆扫描谱域光学相干断层成像（SD-OCT）是诊断视网膜疾病的最佳工具之一。这项技术提供了更全面的细节视网膜形态和层周围的视盘神经头（ONH）。由于视网膜层的手动标记可能是繁琐且耗时的，因此需要准确且鲁棒的自动分割方法来在视网膜疾病评估（例如青光眼）中提供这些层的厚度评估。所提出的方法通过在ONH周围采集的圆形SD-OCT扫描中执行视网膜层边界的分割来达到此目的。通过调整图切割分割技术，包括核诱导空间和基于连续乘子的最大流算法来检测层。实验结果表明，该方法能够有效地检测图像中视网膜层的边界，具有较好的鲁棒性。视网膜神经纤维层厚度的平均均方根误差（RMSE）为0.0835 +/- 0.0495，平均Dice系数为0.9468 +/- 0.0705像素，所提出的方法证明了与手动注释的有效一致性。(C)2015美国光学学会
Circular scan Spectral-Domain Optic Coherence Tomography imaging (SD-OCT) is one of the best tools for diagnosis of retinal diseases. This technique provides more comprehensive detail of the retinal morphology and layers around the optic disc nerve head (ONH). Since manual labelling of the retinal layers can be tedious and time consuming, accurate and robust automated segmentation methods are needed to provide the thickness evaluation of these layers in retinal disorder assessments such as glaucoma. The proposed method serves this purpose by performing the segmentation of retinal layers boundaries in circular SD-OCT scans acquired around the ONH. The layers are detected by adapting a graph cut segmentation technique that includes a kernel-induced space and a continuous multiplier based max-flow algorithm. Results from scan images acquired with Spectralis (Heidelberg Engineering, Germany) prove that the proposed method is robust and efficient in detecting the retinal layers boundaries in images. With a mean root-mean-square error (RMSE) of 0.0835 +/- 0.0495 and an average Dice coefficient of 0.9468 +/- 0.0705 pixels for the retinal nerve fibre layer thickness, the proposed method demonstrated effective agreement with manual annotations. (C) 2015 Optical Society of America