Automatic Segmentation in Multiple OCT Layers For Stargardt Disease Characterization Via Deep Learning.
Automatic Segmentation in Multiple OCT Layers For Stargardt Disease Characterization Via Deep Learning.
复制标题
DOI：
10.1167/tvst.10.4.24
复制
发表时间：
2021-04-01
影响因子：
3
通讯作者：
Hu Z
中科院分区：
文献类型：
--
作者：
Mishra Z;Wang Z;Sadda SR;Hu Z
本研究旨在对光谱域光学相干断层扫描（SD-OCT）图像上的11个视网膜层和Stargardt相关特征进行自动分割，并分析正常眼睛和诊断为Stargardt病的眼睛之间的差异。自动分割是通过应用深度学习-最短路径（DL-SP）框架来完成的，这是一种最短路径分割方法，由深度学习全卷积神经网络增强。为了比较正常眼睛和诊断为Stargardt病的眼睛，生成与视网膜外层相关的各种视网膜层厚度和强度特征图。自动化DL-SP的方法实现了一个亚像素精度范围内的所有层的平均差异相比，手动跟踪层的专家分级。该算法在Stargardt特征的边界位置上分别实现了-0.11 ± 4.17像素和1.92 ± 3.71像素的平均值和绝对平均值差。在生成的几个特征图中，很容易看到震颤和萎缩性病变的特征性Stargardt特征。据我们所知，这是第一个自动化算法的11视网膜层分割OCT的眼睛与Stargardt病，而且，诊断与Stargardt病和正常的眼睛之间发现的特征差异可能会通知新的见解和更好地理解视网膜特征形态学变化引起的Stargardt病。自动算法的性能和使用算法分割发现的特征差异支持SD-OCT在Stargardt病定量监测中的未来应用。
This study sought to perform automated segmentation of 11 retinal layers and Stargardt-associated features on spectral-domain optical coherence tomography (SD-OCT) images and to analyze differences between normal eyes and eyes diagnosed with Stargardt disease. Automated segmentation was accomplished through application of the deep learning–shortest path (DL-SP) framework, a shortest path segmentation approach that is enhanced by a deep learning fully convolutional neural network. To compare normal eyes and eyes diagnosed with Stargardt disease, various retinal layer thickness and intensity feature maps associated with the outer retinal layers were generated. The automated DL-SP approach achieved a mean difference within a subpixel accuracy range for all layers when compared to manually traced layers by expert graders. The algorithm achieved mean and absolute mean differences in border positions for Stargardt features of −0.11 ± 4.17 pixels and 1.92 ± 3.71 pixels, respectively. In several of the feature maps generated, the characteristic Stargardt features of flecks and atrophic-appearing lesions were readily visualized. To the best of our knowledge, this is the first automated algorithm for 11 retinal layer segmentation on OCT in eyes with Stargardt disease, and, furthermore, the feature differences found between eyes diagnosed with Stargardt disease and normal eyes may inform new insights and the better understanding of retinal characteristic morphologic changes caused by Stargardt disease. The automated algorithm's performance and the feature differences found using the algorithm's segmentation support the future applications of SD-OCT for the quantitative monitoring of Stargardt disease.
登录
查看更多内容
DOI：
10.1109/tpami.2006.19
发表时间：
2006-01-01
影响因子：
23.6
作者：
Li, K;Wu, XD;Sonka, M
通讯作者：
Sonka, M
影响因子：
8.1
作者：
Strauss, Rupert W.;Munoz, Beatriz;Scholl, Hendrik P. N.
通讯作者：
Scholl, Hendrik P. N.
影响因子：
13.7
作者：
Strauss, Rupert W.;Ho, Alex;Scholl, Hendrik P. N.
通讯作者：
Scholl, Hendrik P. N.
影响因子：
4.4
作者：
Binley, Katie;Widdowson, Peter;Mitrophanous, Kyriacos A.
通讯作者：
Mitrophanous, Kyriacos A.
影响因子：
5.1
作者：
Kong, J.;Kim, S-R;Allikmets, R.
通讯作者：
Allikmets, R.