【摘要】 针对糖尿病视网膜病变结构复杂、不同病变尺度差异大等问题，提出一种融合可变形卷积和注意力机制的视网膜多病变分割网络用于糖尿病视网膜多病变自动分割。首先，使用可变形小波编码模块替换原始卷积下采样编码器，以适应病变的不规则形状变化，提取有效特征信息；然后，在瓶颈层引入密集特征感知与聚合模块，通过聚合多个感受野进行多尺度特征的提取，增强深层语义信息；最后，为充分融合解码器输出，提升对边缘信息的识别精度，引入多尺度自适应融合模块对每层解码器输出进行加权，从而获取最准确的分割特征图。在DDR-RLS数据集上进行硬性渗出物、出血点、软性渗出物分割验证，结果发现所提出的网络与原有Unet相比，IoU系数分别提升0.026 2、0.051 8、0.046 5,Dice系数分别提升0.027 1、0.058 1、0.050 4,AUPR值分别提升0.042 3、0.069 1、0.073 4。更多还原

【Abstract】 In view of the complex structure of diabetic retinopathy and the large differences in the scales of different lesions,a novel network which integrates deformable convolution and attention mechanism is proposed for automatic diabetic retinopathy multi-lesion segmentation. Specifically, deformable convolution Haar wavelet transform encoder takes place of the original convolutional downsampling encoder to adapt to the irregular shape changes of lesions and extract effective feature information; a dense feature perception and aggregation module is introduced at the bottleneck layer to extract multiscale features by aggregating multiple receptive fields, thus enhancing deep semantic information; and finally, in order to fully integrate the decoder output and improve the recognition accuracy of edge information, a multi scale adaptive fusion module is used to weight the decoder output of each layer for obtaining the most accurate segmentation feature map. The validation of hard percolation, bleeding point, and soft percolation segmentations on the DDR-RLS dataset reveals that the proposed network shows increases of 0.026 2, 0.051 8 and 0.046 5 in IoU coefficient, 0.027 1, 0.058 1 and 0.050 4 in Dice coefficient, and 0.042 3, 0.069 1 and 0.073 4 in AUPR value, as compared with the original Unet.更多还原