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ATLAS FOR SEGMENTATION OF RETINA LAYERS FROM OCT IMAGES

机译:用于从OCT图像中分离视网膜层的图集

摘要

A method for segmentation of a 3-D medical image uses an adaptive patient-specific atlas and an appearance model for 3-D Optical Coherence Tomography (OCT) data. For segmentation of a medical image of a retina, In order to reconstruct the 3-D patient-specific retinal atlas, a 2-D slice of the 3-D image containing the macula mid-area is segmented first. A 2-D shape prior is built using a series of co-aligned training OCT images. The shape prior is then adapted to the first order appearance and second order spatial interaction MGRF model of the image data to be segmented. Once the macula mid-area is segmented into separate retinal layers in this initial slice, the segmented layers' labels and their appearances are used to segment the adjacent slices. This step is iterated until the complete 3-D medical image is segmented.
机译:用于对3D医学图像进行分割的方法使用自适应患者特定图集和3D光学相干断层扫描(OCT)数据的外观模型。为了分割视网膜的医学图像,为了重建3D患者特定的视网膜图谱,首先将包含黄斑中部区域的3D图像的2D切片进行分割。使用一系列共同对齐的训练OCT图像构建二维形状先验。形状先验然后适应于要分割的图像数据的一阶外观和二阶空间相互作用MGRF模型。一旦将黄斑中部区域分割成该初始切片中的单独的视网膜层,分割后的层的标签及其外观将用于分割相邻的切片。重复此步骤,直到完整的3D医学图像被分割为止。

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