【摘要】 为了提高视网膜神经上皮层脱离自动分割的精度和效率,提出一种基于多分辨率三维图割的自动分割算法.首先通过视网膜厚度变化信息准确定位目标和背景区域,统计目标和背景的灰度分布特征,为图割算法提供先验信息;然后采用三维图割算法对4倍下采样后的频域光学相干断层扫描(SD-OCT)图像进行分割,得到粗分割结果;最后在原始分辨率图像上,对粗分割结果两侧的窄带区域进行三维图割,得到最终分割结果.在18组CirrusSD-OCT数据集上进行分割的实验结果表明,该算法的Dice相似性系数为95.07%,分割一组数据的平均时间为57 s,精度和效率均优于现有算法.更多还原

【Abstract】 In order to improve the accuracy and efficiency of NRD segmentation, we propose a fully automatic segmentation method based on multiscale 3D graph cut algorithm. Firstly, we accurately locate the target and background regions based on changes in retinal thickness, and the grayscale distribution of the target and background provides prior information for the graph cut algorithm. Then, we obtain the coarse segmentation result by performing 3D graph cut algorithm on the downsampling SD-OCT image. Finally, on the original resolution image, we use 3D graph cut algorithm on the narrow-band regions on both sides of the coarse segmentation result to obtain the final segmentation result. The results of segmentation experiments on 18 sets of Cirrus SD-OCT datasets show that the Dice coefficient of the proposed algorithm is 95.07%, and the average time for segmenting a set of data is 57 seconds. Both the accuracy and efficiency of the proposed algorithm are better than existing algorithms.更多还原