分割
人工智能
计算机科学
尺度空间分割
计算机视觉
图像分割
中央凹无血管区
区域增长
基于分割的对象分类
一致性(知识库)
模式识别(心理学)
光学相干层析成像
光学相干断层摄影术
医学
眼科
作者
Zhijin Liang,Junkang Zhang,Cheolhong An
标识
DOI:10.1109/icassp39728.2021.9415070
摘要
Foveal Avascular Zone (FAZ) is a crucial indicator for retinal disease detection and accurate automatic FAZ segmentation has a significant impact in clinical applications. Apart from the binary FAZ segmentation map, a vessel segmentation map can provide further information. To simultaneously implement vessel and accurate FAZ segmentation, an end-to-end trained network is proposed to achieve unsupervised vessel segmentation and supervised FAZ segmentation. Due to the lack of vessel labels, the style transfer with consistency loss is proposed to the vessel segmentation. Then FAZ segmentation is achieved with a U-Net structure based on vessel segmentation. Two superficial layer OCTA image datasets - OCTAGON3 [1] and sFAZDATA datasets [2] - are used to evaluate the proposed method. We achieve the Dice scores of 0.9263 and 0.9784, which are better than those from other approaches.
科研通智能强力驱动
Strongly Powered by AbleSci AI