School of Automation, Central South University, Changsha, Hunan 410083, China.
School of Automation, Central South University, Changsha, Hunan 410083, China.
Comput Methods Programs Biomed. 2022 May;218:106732. doi: 10.1016/j.cmpb.2022.106732. Epub 2022 Mar 5.
Retinal image segmentation can help clinicians detect pathological disorders by studying changes in retinal blood vessels. This early detection can help prevent blindness and many other vision impairments. So far, several supervised and unsupervised methods have been proposed for the task of automatic blood vessel segmentation. However, the sensitivity and the robustness of these methods can be improved by correctly classifying more vessel pixels.
We proposed an automatic, retinal blood vessel segmentation method based on the U-net architecture. This end-to-end framework utilizes preprocessing and a data augmentation pipeline for training. The architecture utilizes multiscale input and multioutput modules with improved skip connections and the correct use of dilated convolutions for effective feature extraction. In multiscale input, the input image is scaled down and concatenated with the output of convolutional blocks at different points in the encoder path to ensure the feature transfer of the original image. The multioutput module obtains upsampled outputs from each decoder block that are combined to obtain the final output. Skip paths connect each encoder block with the corresponding decoder block, and the whole architecture utilizes different dilation rates to improve the overall feature extraction.
The proposed method achieved an accuracy: of 0.9680, 0.9694, and 0.9701; sensitivity of 0.8837, 0.8263, and 0.8713; and Intersection Over Union (IOU) of 0.8698, 0.7951, and 0.8184 with three publicly available datasets: DRIVE, STARE, and CHASE, respectively. An ablation study is performed to show the contribution of each proposed module and technique.
The evaluation metrics revealed that the performance of the proposed method is higher than that of the original U-net and other U-net-based architectures, as well as many other state-of-the-art segmentation techniques, and that the proposed method is robust to noise.
视网膜图像分割可以帮助临床医生通过研究视网膜血管的变化来检测病理病变。这种早期检测有助于预防失明和许多其他视力障碍。到目前为止，已经提出了几种用于自动血管分割的有监督和无监督方法。然而，通过正确分类更多的血管像素，可以提高这些方法的灵敏度和鲁棒性。
我们提出了一种基于 U 形网络结构的自动视网膜血管分割方法。这个端到端的框架利用预处理和数据增强管道进行训练。该架构利用多尺度输入和多输出模块，带有改进的跳过连接和正确使用扩张卷积，以实现有效的特征提取。在多尺度输入中，输入图像缩小，并与编码器路径中不同点的卷积块的输出连接，以确保原始图像的特征传递。多输出模块从每个解码器块中获得上采样输出，并将它们组合以获得最终输出。跳过路径将每个编码器块与相应的解码器块连接起来，整个架构利用不同的扩张率来提高整体特征提取。
所提出的方法在三个公开数据集 DRIVE、STARE 和 CHASE 上分别达到了 0.9680、0.9694 和 0.9701 的准确率、0.8837、0.8263 和 0.8713 的敏感性以及 0.8698、0.7951 和 0.8184 的交并比。进行了消融研究以显示每个提出的模块和技术的贡献。
评估指标表明，所提出的方法的性能高于原始 U 形网络和其他基于 U 形网络的架构以及许多其他最新的分割技术，并且该方法对噪声具有鲁棒性。
