Adaptive-Guided-Coupling-Probability Level Set for Retinal Layer Segmentation

Quantitative assessment of retinal layer thickness in spectral domain-optical coherence tomography (SD-OCT) images is vital for clinicians to determine the degree of ophthalmic lesions. However, due to the complex retinal tissues, high-level speckle noises and low intensity constraint, how to accurately recognize the retinal layer structure still remains a challenge. To overcome this problem, this paper proposes an adaptive-guided-coupling-probability level set method for retinal layer segmentation in SD-OCT images. Specifically, based on Bayes's theorem, each voxel probability representation is composed of two probability terms in our method. The first term is constructed as neighborhood Gaussian fitting distribution to characterize intensity information for each intra-retinal layer. The second one is boundary probability map generated by combining anatomical priors and adaptive thickness information to ensure surfaces evolve within a proper range. Then, the voxel probability representation is introduced into the proposed segmentation framework based on coupling probability level set to detect layer boundaries. A total of 1792 retinal B-scan images from 4 SD-OCT cubes in healthy eyes, 5 cubes in abnormal eyes with central serous chorioretinaopathy and 5 SD-OCT cubes in abnormal eyes with age-related macular disease are used to evaluate the proposed method. The experiment demonstrates that the segmentation results obtained by the proposed method have a good consistency with ground truth, and the proposed method outperforms six methods in the layer segmentation of uneven retinal SD-OCT images.

Authors

• Yue Sun Intelligent Medical Computing Laboratory, Faculty of Applied Sciences, Macao Polytechnic University, Macao, China
• Sijie Niu
• Xizhan Gao
• Jie Su Zhejiang University of Technology
• Jiwen Dong
• Yuehui Chen
• Li Wang

Keywords

• Retina
• Image segmentation
• Level set
• Diseases
• Feature extraction
• Mathematical model
• Active contours
• Retinal Layer
• Layer Segmentation
• Retinal Layer Segmentation
• Normal Distribution
• Layer Thickness
• Good Consistency
• Boundary Layer
• Segmentation Method
• Segmentation Results
• Retinal Images
• Intensity Information
• Spectral-domain Optical Coherence Tomography
• High Noise Levels
• Anatomical Information
• Retinal Structure
• Healthy Eyes
• Abnormal Eye
• Segmentation Framework
• Probabilistic Terms
• Central Serous Chorioretinopathy
• Fully Convolutional Network
• Retinal Segmentation
• Active Contour
• Normal Eyes
• OCT Images
• Detection Results
• Hard Constraints
• Fluid Region
• Comparative Method
• Spectral domain-optical coherence tomography
• neighborhood gaussian fitting distribution
• anatomical priors and level set
• Bayes Theorem
• Humans
• Macular Degeneration
• Probability
• Tomography, Optical Coherence