Li Zhenwei, Jia Mengli, Yang Xiaoli, Xu Mengying
School of Medical Technology and Engineering, Henan University of Science and Technology, Luoyang 471023, China.
Micromachines (Basel). 2021 Nov 29;12(12):1478. doi: 10.3390/mi12121478.
The accurate segmentation of retinal blood vessels in fundus is of great practical significance to help doctors diagnose fundus diseases. Aiming to solve the problems of serious segmentation errors and low accuracy in traditional retinal segmentation, a scheme based on the combination of U-Net and Dense-Net was proposed. Firstly, the vascular feature information was enhanced by fusion limited contrast histogram equalization, median filtering, data normalization and multi-scale morphological transformation, and the artifact was corrected by adaptive gamma correction. Secondly, the randomly extracted image blocks are used as training data to increase the data and improve the generalization ability. Thirdly, stochastic gradient descent was used to optimize the Dice loss function to improve the segmentation accuracy. Finally, the Dense-U-net model was used for segmentation. The specificity, accuracy, sensitivity and AUC of this algorithm are 0.9896, 0.9698, 0.7931, 0.8946 and 0.9738, respectively. The proposed method improves the segmentation accuracy of vessels and the segmentation of small vessels.
眼底视网膜血管的精确分割对于帮助医生诊断眼底疾病具有重要的现实意义。针对传统视网膜分割中分割误差严重、准确率低的问题，提出了一种基于U-Net和Dense-Net相结合的方案。首先，通过融合有限对比度直方图均衡化、中值滤波、数据归一化和多尺度形态变换增强血管特征信息，并通过自适应伽马校正校正伪影。其次，将随机提取的图像块用作训练数据，以增加数据量并提高泛化能力。第三，使用随机梯度下降优化Dice损失函数以提高分割精度。最后，使用Dense-U-net模型进行分割。该算法的特异性、准确率、灵敏度、AUC分别为0.9896、0.9698、0.7931、0.8946和0.9738。所提方法提高了血管的分割精度以及小血管的分割效果。
