论文标题
利用转移学习和自定义损失函数从视网膜图像中进行光盘分割
Utilizing Transfer Learning and a Customized Loss Function for Optic Disc Segmentation from Retinal Images
论文作者
论文摘要
从视网膜图像中对视盘进行精确分割对于提取视网膜特征至关重要，而视网膜特征可能与视网膜条件（例如青光眼）高度相关。在本文中，我们提出了一种基于深度学习的方法，能够在高精度的视网膜底面图像下分割视盘。我们的方法利用了基于UNET的模型，该模型具有在Imagenet数据集上训练的VGG16编码器。这项研究可以与针对VGG16模型的自定义，所采用的数据集的多样性，圆盘分割的持续时间，所使用的损耗函数的持续时间以及训练我们的模型所需的参数数量区分开来。我们的方法对七个公开可用的数据集进行了测试，该数据集由私人诊所的数据集增加，该数据集通过为此目的构建的Web门户网站注释了两名验光医生。我们的精度为99.78％\％，骰子系数为94.73 \％，用于在0.03秒内从视网膜图像中进行圆盘分割。从综合实验获得的结果表明，我们对从不同来源获得的视网膜图像进行盘式分割的方法的鲁棒性。
Accurate segmentation of the optic disc from a retinal image is vital to extracting retinal features that may be highly correlated with retinal conditions such as glaucoma. In this paper, we propose a deep-learning based approach capable of segmenting the optic disc given a high-precision retinal fundus image. Our approach utilizes a UNET-based model with a VGG16 encoder trained on the ImageNet dataset. This study can be distinguished from other studies in the customization made for the VGG16 model, the diversity of the datasets adopted, the duration of disc segmentation, the loss function utilized, and the number of parameters required to train our model. Our approach was tested on seven publicly available datasets augmented by a dataset from a private clinic that was annotated by two Doctors of Optometry through a web portal built for this purpose. We achieved an accuracy of 99.78\% and a Dice coefficient of 94.73\% for a disc segmentation from a retinal image in 0.03 seconds. The results obtained from comprehensive experiments demonstrate the robustness of our approach to disc segmentation of retinal images obtained from different sources.
