目的 提出一种基于多尺度卷积神经网络(CNN)的眼底图像病灶检测算法,并探讨其在糖尿病视网膜病变(DR)中的应用.方法 对比现有眼底病灶检测方法,提出一种基于CNN的眼底图像病灶检测算法.本算法不仅克服了基于阈值分割和形态学分割方法鲁棒性差的问题,同时在不依赖人工逐像素标注的前提下,采用多尺度图像块的检测思路,显著提升检测器对小病灶目标检测的性能.此外,提出的新型损失函数在弱标签、小数据集的条件下,实现多类型、高准确率的DR病灶检测.结果 从病灶水平来看,该算法对硬性渗出病灶检测的敏感性和特异性分别为92.17％和97.17％;相较于单尺度方法,本研究中提出的多尺度方法的敏感性和准确率分别提升了7.41％和5.02％;在公开数据集IDRiD上较其他检测方法特异性提高了55.82％.本方法能够将眼底图像中的病变有效地检测出来,且能够给出病灶的基本范围,对于有大量病灶眼底图像的平均检测时间为1.59 s.结论 基于多尺度CNN的眼底图像病灶检测算法能够快速、可靠地识别出眼底图像中的DR病灶并标注出病灶的位置信息,降低主观因素的影响,辅助临床医生更加高效、准确地进行DR病变筛查.%Objective To propose a multi-scale convolutional neural network ( CNN) based lesions detection method of fundus image,and evaluate its application in diabetic retinopathy ( DR) assisted diagnosis. Methods A multi-scale CNN based on lesions detection method of fundus image was proposed. Compared with the existing detection methods,the problem of poor robustness based on threshold segmentation and morphological segmentation was overcome. The idea of multi-scale grids detection without relying on manual pixel-by-pixel labeling was adopted in this algorithm,and the detection performance of small lesions was significantly improved. In addition, multiple DR lesions with high accuracy could be detected by the proposed loss function under the condition of weak labels and small data sets. Results At the level of lesions,the sensitivity and specificity of hard exudation lesions detection were 92. 17％ and 97. 17％,respectively. Compared with single-scale method,the sensitivity and accuracy of multi-scale method proposed in this paper increased by 7. 41％ and 5. 02％,respectively,and compared with other algorithm using the same public dataset IDRiD, the specificity of this algorithm increased by 55. 82％. This method could effectively detect the lesions in fundus images,and could give the basic range of the lesions. The average detection time of fundus images with a large number of lesions was 1. 59 seconds. Conclusions The DR lesions in the fundus image can be quickly and reliably identified,the location information of the lesions can be marked,and the influence of subjective factors can be reduced by using this algorithm, and it can be used to assist the clinician to conduct more effectively.
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