论文标题
通过加权损失函数和从视网膜图像进行船舶分割的集体归一化进行转移学习
Transfer Learning Through Weighted Loss Function and Group Normalization for Vessel Segmentation from Retinal Images
论文作者
论文摘要
血管的血管结构在诊断视网膜状况（如青光眼和糖尿病性视网膜病）中很重要。这些容器的准确分割可以帮助检测视网膜对象，例如视盘和视杯，因此确定这些区域是否有损坏。此外，血管的结构可以帮助诊断青光眼。数字成像和计算机视觉技术的快速开发增加了开发分割视网膜血管的方法的潜力。在本文中，我们提出了一种分割视网膜血管的方法，该视网膜在转移学习和转移学习以及使用深度学习的方法中。我们对U-NET结构进行了调整，以使用自定义的InceptionV3作为编码器，并使用多个Skip Connections形成解码器。此外，我们使用加权损失函数来处理视网膜图像中类不平衡问题的问题。此外，我们为该字段贡献了一个新数据集。我们在六个公开可用的数据集和一个新创建的数据集上测试了我们的方法。我们的平均准确度为95.60％，骰子系数为80.98％。从综合实验获得的结果表明，我们从不同来源获得的视网膜图像中分割血管的方法的鲁棒性。与其他方法相比，我们的方法可以提高细分精度。
The vascular structure of blood vessels is important in diagnosing retinal conditions such as glaucoma and diabetic retinopathy. Accurate segmentation of these vessels can help in detecting retinal objects such as the optic disc and optic cup and hence determine if there are damages to these areas. Moreover, the structure of the vessels can help in diagnosing glaucoma. The rapid development of digital imaging and computer-vision techniques has increased the potential for developing approaches for segmenting retinal vessels. In this paper, we propose an approach for segmenting retinal vessels that uses deep learning along with transfer learning. We adapted the U-Net structure to use a customized InceptionV3 as the encoder and used multiple skip connections to form the decoder. Moreover, we used a weighted loss function to handle the issue of class imbalance in retinal images. Furthermore, we contributed a new dataset to this field. We tested our approach on six publicly available datasets and a newly created dataset. We achieved an average accuracy of 95.60% and a Dice coefficient of 80.98%. The results obtained from comprehensive experiments demonstrate the robustness of our approach to the segmentation of blood vessels in retinal images obtained from different sources. Our approach results in greater segmentation accuracy than other approaches.
