1. FFU-Net: Feature Fusion U-Net for Lesion Segmentation of Diabetic Retinopathy
- Author
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Nuo Zhang, Pingping Wei, Zhuming Zhou, Yifei Xu, Meizi Zhang, and Li Xiao
- Subjects
Channel (digital image) ,Computer science ,02 engineering and technology ,Diagnostic Techniques, Ophthalmological ,Fundus (eye) ,Retina ,General Biochemistry, Genetics and Molecular Biology ,030218 nuclear medicine & medical imaging ,Lesion ,03 medical and health sciences ,Deep Learning ,0302 clinical medicine ,Image Interpretation, Computer-Assisted ,0202 electrical engineering, electronic engineering, information engineering ,medicine ,Humans ,Segmentation ,Block (data storage) ,Diabetic Retinopathy ,General Immunology and Microbiology ,business.industry ,Deep learning ,Pattern recognition ,General Medicine ,Diabetic retinopathy ,medicine.disease ,Benchmark (computing) ,Medicine ,020201 artificial intelligence & image processing ,Artificial intelligence ,medicine.symptom ,business ,Research Article - Abstract
Diabetic retinopathy is one of the main causes of blindness in human eyes, and lesion segmentation is an important basic work for the diagnosis of diabetic retinopathy. Due to the small lesion areas scattered in fundus images, it is laborious to segment the lesion of diabetic retinopathy effectively with the existing U-Net model. In this paper, we proposed a new lesion segmentation model named FFU-Net (Feature Fusion U-Net) that enhances U-Net from the following points. Firstly, the pooling layer in the network is replaced with a convolutional layer to reduce spatial loss of the fundus image. Then, we integrate multiscale feature fusion (MSFF) block into the encoders which helps the network to learn multiscale features efficiently and enrich the information carried with skip connection and lower-resolution decoder by fusing contextual channel attention (CCA) models. Finally, in order to solve the problems of data imbalance and misclassification, we present a Balanced Focal Loss function. In the experiments on benchmark dataset IDRID, we make an ablation study to verify the effectiveness of each component and compare FFU-Net against several state-of-the-art models. In comparison with baseline U-Net, FFU-Net improves the segmentation performance by 11.97%, 10.68%, and 5.79% on metrics SEN, IOU, and DICE, respectively. The quantitative and qualitative results demonstrate the superiority of our FFU-Net in the task of lesion segmentation of diabetic retinopathy.
- Published
- 2021