期刊
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
卷 88, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.bspc.2023.105050
关键词
Retinal image; Convolutional neural network (CNN); Segmentation; Diabetic retinopathy (DR)
This paper proposes a CNN-based approach for segmenting four typical DR lesions simultaneously, achieving competitive performance. This approach is significant for DR lesion segmentation and has potential in other segmentation tasks.
The segmentation of diabetic retinopathy (DR) lesions is important for large-scale screening using color fundus photography (CFP) images. The difficulty of this task is that the DR lesions have various sizes, shapes, and intensities. Traditional handcrafted feature-based approaches are unsatisfactory, and recent deep-learning-based approaches ignore the features of DR lesions. In this paper, we propose a segmentation approach that segments four typical DR lesions simultaneously based on convolutional neural networks (CNN). The raw CFP image is first pre-processed and resized to different sizes. A set of fully convolutional neural networks (FCN) with various input sizes are then trained to extract the lesions from different scales. An auxiliary CNN is then introduced to fuse the output from these FCN and refine the segmentation result. We conducted our experiments on one local dataset and four public datasets including IDRiD, DDR, E-ophtha, and DIARETDB1. The results of the area under the precision-recall-curve (AUPR) and the dice similarity coefficient (DSC) show that our approach achieves competitive performance. The improvement in performance indicates that this approach is beneficial to DR lesion segmentation and has potential in other segmentation tasks.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据