基于多任务学习的糖尿病视网膜病变图像分割

Diabetic Retinopathy Image Segmentation Based on Multi-task Learning

针对糖尿病视网膜病变(DR)图像,提出了一种基于多任务学习的图像多分类分割方法.首先,通过Otsu阈值算法将大部分无病灶信息像素去除;其次,通过滑动窗口切割的方法将图像切分为若干小尺寸的图像,以解决医学图像分辨率过大以及病灶在图像中占比较小的问题;再次,将不存在病灶的子图剔除,以增大含病灶子图的比例;最后,利用UNet++多任务学习属性,并且用转置卷积代替传统上采样,进行多输出多病灶的图像分割.通过在国际公开的IDRID和DDR数据集上进行验证,在IDRi D上取得0.7131的m AUPR,在DDR上取得0.5691的m AUPR.

A multi-class image segmentation method based on multi-task learning is proposed for diabetic retinopathy(DR)images.Specifically,the dominant pixels without lesion information are removed by the Otsu thresholding algorithm;subsequently,the image is segmented into several small-sized images by the method of sliding window segmentation to solve the problems that the resolution of medical images is too large and the proportion of lesions in the image is small;then,sub-images without lesions are eliminated to increase the proportion of those with lesions;finally,multi-output multi-lesion image segmentation is performed by leveraging the multi-task learning properties of UNet++and replacing traditional upsampling with transposed convolution.When the proposed method is verified on the international public Indian diabetic retinopathy image dataset(IDRID)and dataset for diabetic retinopathy(DDR),it achieves a mean area under precision-recall curve(mAUPR)of 0.7131 on IDRID and an mAUPR of 0.5691 on DDR.