Automatic Retinal Layer Segmentation of OCT Images With Central Serous Retinopathy

In this paper, an automatic method is reported for simultaneously segmenting layers and fluid in 3-D OCT retinal images of subjects suffering from central serous retinopathy. To enhance contrast between adjacent layers, multiscale bright and dark layer detection filters are proposed. Due to appearance of serous fluid or pigment epithelial detachment caused fluid, contrast between adjacent layers is often reduced, and also large morphological changes are caused. In addition, 24 features are designed for random forest classifiers. Then, 8 coarse surfaces are obtained based on the trained random forest classifiers. Finally, a hypergraph is constructed based on the smoothed image and the layer structure detection responses. A modified live wire algorithm is proposed to accurately detect surfaces between retinal layers, even though OCT images with fluids are of low contrast and layers are largely deformed. The proposed method was evaluated on 48 spectral domain OCT images with central serous retinopathy. The experimental results showed that the proposed method outperformed the state-of-art methods with regard to layers and fluid segmentation.

[1]  Xinjian Chen,et al.  3D Fast Automatic Segmentation of Kidney Based on Modified AAM and Random Forest , 2016, IEEE Transactions on Medical Imaging.

[2]  J. A. Noble,et al.  Investigation of the Role of Feature Selection and Weighted Voting in Random Forests for 3-D Volumetric Segmentation , 2014, IEEE Transactions on Medical Imaging.

[3]  K. A. Vermeer,et al.  Automated segmentation by pixel classification of retinal layers in ophthalmic OCT images , 2011, Biomedical optics express.

[4]  Xinjian Chen,et al.  CorteXpert: A model‐based method for automatic renal cortex segmentation , 2017, Medical Image Anal..

[5]  Jerry L Prince,et al.  Retinal layer segmentation of macular OCT images using boundary classification , 2013, Biomedical optics express.

[6]  Milan Sonka,et al.  Stratified Sampling Voxel Classification for Segmentation of Intraretinal and Subretinal Fluid in Longitudinal Clinical OCT Data , 2015, IEEE Transactions on Medical Imaging.

[7]  Xiaodong Wu,et al.  Intraretinal Layer Segmentation of Macular Optical Coherence Tomography Images Using Optimal 3-D Graph Search , 2008, IEEE Transactions on Medical Imaging.

[8]  Taimur Hassan,et al.  Structure tensor based automated detection of macular edema and central serous retinopathy using optical coherence tomography images. , 2016, Journal of the Optical Society of America. A, Optics, image science, and vision.

[9]  Shijian Lu,et al.  Automated layer segmentation of optical coherence tomography images , 2010, 2010 5th IEEE Conference on Industrial Electronics and Applications.

[10]  Liang Liu,et al.  Automated volumetric segmentation of retinal fluid on optical coherence tomography. , 2016, Biomedical optics express.

[11]  Qi Yang,et al.  Automated layer segmentation of macular OCT images using dual-scale gradient information. , 2010, Optics express.

[12]  Jelena Novosel,et al.  Locally-adaptive loosely-coupled level sets for retinal layer and fluid segmentation in subjects with central serous retinopathy , 2016, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI).

[13]  Milan Sonka,et al.  Three-Dimensional Analysis of Retinal Layer Texture: Identification of Fluid-Filled Regions in SD-OCT of the Macula , 2010, IEEE Transactions on Medical Imaging.

[14]  Jelena Novosel,et al.  Loosely coupled level sets for simultaneous 3D retinal layer segmentation in optical coherence tomography , 2015, Medical Image Anal..

[15]  Jayaram K. Udupa,et al.  User-Steered Image Segmentation Paradigms: Live Wire and Live Lane , 1998, Graph. Model. Image Process..

[16]  Gábor Márk Somfai,et al.  Real-Time Automatic Segmentation of Optical Coherence Tomography Volume Data of the Macular Region , 2015, PloS one.

[17]  松本 英孝.  Outer nuclear layer thickness at the fovea determines visual outcomes in resolved central serous chorioretinopathy , 2010 .

[18]  Luis de Sisternes,et al.  Automated intraretinal segmentation of SD-OCT images in normal and age-related macular degeneration eyes. , 2017, Biomedical optics express.

[19]  Junjie Bai,et al.  Optimal Multiple Surface Segmentation With Shape and Context Priors , 2013, IEEE Transactions on Medical Imaging.

[20]  Christian Ahlers,et al.  Alterations of intraretinal layers in acute central serous chorioretinopathy , 2009, Acta ophthalmologica.

[21]  Xiaodong Wu,et al.  Automated 3-D Intraretinal Layer Segmentation of Macular Spectral-Domain Optical Coherence Tomography Images , 2009, IEEE Transactions on Medical Imaging.

[22]  Ross T. Whitaker,et al.  Variable-conductance, level-set curvature for image denoising , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[23]  Richard Hackel,et al.  DETECTION OF RETINAL METABOLIC STRESS RESULTING FROM CENTRAL SEROUS RETINOPATHY , 2009, Retina.

[24]  S. Sadda,et al.  Proposed lexicon for anatomic landmarks in normal posterior segment spectral-domain optical coherence tomography: the IN•OCT consensus. , 2014, Ophthalmology.

[25]  Jelena Novosel,et al.  Joint Segmentation of Retinal Layers and Focal Lesions in 3-D OCT Data of Topologically Disrupted Retinas , 2017, IEEE Transactions on Medical Imaging.

[26]  Sina Farsiu,et al.  Automatic segmentation of up to ten layer boundaries in SD-OCT images of the mouse retina with and without missing layers due to pathology. , 2014, Biomedical optics express.

[27]  Pascal A. Dufour,et al.  Graph-Based Multi-Surface Segmentation of OCT Data Using Trained Hard and Soft Constraints , 2013, IEEE Transactions on Medical Imaging.

[28]  Milan Sonka,et al.  Intra-retinal layer segmentation of 3D optical coherence tomography using coarse grained diffusion map , 2012, Medical Image Anal..

[29]  Jayaram K. Udupa,et al.  A 3D generalization of user-steered live-wire segmentation , 2000, Medical Image Anal..

[30]  Xinjian Chen,et al.  Automated 3-D Retinal Layer Segmentation of Macular Optical Coherence Tomography Images With Serous Pigment Epithelial Detachments , 2015, IEEE Transactions on Medical Imaging.

[31]  Mirza Faisal Beg,et al.  Automatic detection of subretinal fluid and sub-retinal pigment epithelium fluid in optical coherence tomography images , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[32]  Jack Sklansky,et al.  Finding the convex hull of a simple polygon , 1982, Pattern Recognit. Lett..

[33]  Xinjian Chen,et al.  Three-Dimensional Segmentation of Fluid-Associated Abnormalities in Retinal OCT: Probability Constrained Graph-Search-Graph-Cut , 2012, IEEE Transactions on Medical Imaging.

[34]  Xinjian Chen,et al.  Automatic Liver Segmentation Based on Shape Constraints and Deformable Graph Cut in CT Images , 2015, IEEE Transactions on Image Processing.