提出了一种改进的多任务学习方法,网络的主结构由特征提取网络和分别进行视盘分割与视盘定位的双路径网络组成,通过端到端的训练与测试可以实现眼底图像视盘自动分割与定位相结合的多任务目的。在特征提取网络的编码阶段利用密集连接提取眼底图像视盘的上下文特征。视盘分割任务是依靠解码阶段逐步恢复原来的图像分辨率并获取整个视盘轮廓,视盘中心定位任务由空洞空间金字塔模块和金字塔池化模块来进一步提取视盘抽象特征,得到精准的视盘中心坐标。对350幅眼底图像进行了视盘分割和中心定位,实验结果表明:该方法自动分割的视盘结果与手动标注视盘区域的Dice系数为0.965,自动定位的视盘中心坐标与手动标记的视盘中心的平均绝对距离为0.191 mm(34.7像素)。
In this paper, an improved multi-task learning method is proposed. The main structure of proposed network contains a feature extraction network and a dual path network for optic disc segmentation and location respectively. And through end-to-end training and testing, the multi-task purpose of automatic optic disc segmentation and location can be achieved. In the decoding phase of the feature extraction network, a dense layer is used to extract the context features of fundus images. The optic disc segmentation task relies on the decoding stage to gradually restore original image resolution and obtain the entire optic disc outline, whereas the optic disc localization task is to obtain accurate optic disc center coordinates by extracting context features of fundus images with atrous space pyramid module and pyramid pooling module. Optic disc segmentation and center localization of 350 fundus images are demonstrated. Experimental results show that the Dice coefficient between the automatically segmented optic disc results and manually marked optic disc areas is 0.965, and the average distance between the automatic localization and the manually marked optic disc center is 0.191 mm (34.7 pixels).
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