基于紧凑混合网络的视网膜血管自动分割

针对视网膜血管分割困难及时间复杂度高等问题,提出一种可以兼顾分割速度和准确度,同时结构非对称的视网膜血管分割模型,即紧凑混合网络(compact mixed network,CMNet).可变形卷积能够提取复杂多变的血管结构,并且混合深度卷积中的大核在增大感受野的同时能够改善分割质量,首先在此基础上提出一种轻量级混合瓶...

Automatic segmentation of retinal vessel via compact mixed network

To address the difficulty and high time-complexity of retinal vessel segmentation, an asymmetric model called compact mixed network(CMNet) is proposed, which is capable of achieving trade-off between speed and accuracy. Firstly, considering the ability of deformable convolution to extract complex and variable vascular structure, and that large kernel in mixed depthwise convolution can further improve segmentation quality while increasing the receptive field, we propose a lightweight mixed bottleneck module. Then, an adaptive feature layer fusion is proposed to further improve the spatial mapping capability of the model. Finally, the vessel segmentation performance is analyzed quantitatively and qualitatively. The AUC metrics are 0.9840, 0.9879 and 0.9853 for DRIVE, CHASE_DB1 and HRF benchmark datasets, respectively, indicating that the proposed algorithm is able to obtain highly accurate segmentation results. Furthermore, with an input resolution of 512times512, the model achieves a frame rate of 33 FPS on a single V100 GPU, which further indicates its suitability for rapid clinical deployment.