School of Software, Xi'an Jiaotong University, 710054, Xi'an, Shaanxi, China.
Huiyichen Inc. 1703, Block 1, No 1388, Jiulonghu Ave, 330038 Nanchang, Jiangxi, China.
Biomed Res Int. 2021 Jan 2;2021:6644071. doi: 10.1155/2021/6644071. eCollection 2021.
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.
糖尿病性视网膜病变是导致人类失明的主要原因之一，而病变分割是糖尿病性视网膜病变诊断的重要基础工作。由于眼底图像中病变区域较小且分散，现有的 U-Net 模型难以有效地对病变进行分割。本文提出了一种名为 FFU-Net（特征融合 U-Net）的新型病变分割模型，该模型从以下几个方面对 U-Net 进行了增强。首先，网络中的池化层被替换为卷积层，以减少眼底图像的空间损失。然后，我们将多尺度特征融合（MSFF）块集成到编码器中，帮助网络有效地学习多尺度特征，并通过融合上下文通道注意力（CCA）模型来丰富带有跳过连接和低分辨率解码器的信息。最后，为了解决数据不平衡和分类错误的问题，我们提出了一种平衡焦点损失函数。在基准数据集 IDRID 上的实验中，我们进行了消融研究以验证每个组件的有效性，并将 FFU-Net 与几种最先进的模型进行了比较。与基线 U-Net 相比，FFU-Net 在 SEN、IOU 和 DICE 这三个指标上的分割性能分别提高了 11.97%、10.68%和 5.79%。定量和定性结果表明，我们的 FFU-Net 在糖尿病性视网膜病变病变分割任务中具有优越性。
