多特征尺度融合改进Faster-RCNN视网膜微动脉瘤自动检测算法  

Improved Faster-RCNN Based on Multi Feature Scale Fusion for Automatic Detection of Microaneurysms in Retina

作　　者：高玮玮 杨亦乐 方宇 樊博 宋楠 GAO Weiwei;YANG Yile;FANG Yu;FAN Bo;SONG Nan(Institute of Mechanical and Automotive Engineering,Shanghai University of Engineering Science,Shanghai 201620,China;Department of Ophthalmology,Eye,Ear,Nose and Throat Hospital of Fudan University,Shanghai 200031,China)

机构地区：[1]上海工程技术大学机械与汽车工程学院,上海201620 [2]复旦大学附属眼耳鼻喉医院眼科,上海200031

出　　处：《光子学报》2023年第4期220-231,共12页Acta Photonica Sinica

基　　金：国家自然科学基金(No.61703268)。

摘　　要：视网膜微动脉瘤的检测对于早期发现糖尿病视网膜病变等重要疾病至关重要,但该病灶尺寸相对较小,属于眼底图像中的微小目标,现有的微动脉瘤检测算法难以实现该病灶的精准检测,为此提出了基于多特征尺度融合的改进Faster-RCNN微动脉瘤自动检测算法。该算法在Faster-RCNN网络模型的基础上,首先采用多特征尺度融合对特征提取网络与RPN结构进行改进以提高网络对于微小目标特征的利用;然后,通过感兴趣区域齐平池化以消除感兴趣区域池化过程中引入的量化误差;最后,通过对损失函数中的smooth L1损失函数进行重新设计得到平衡L1损失函数以实现损失函数优化,从而有效降低大梯度难学样本与小梯度易学样本间的不平衡问题,进而使得模型能够得到更好地训练。针对眼底图像中微动脉瘤的自动检测,将优化后的Faster-RCNN网络模型在Kaggle数据集上进行训练及测试,并与其他方法进行对比。实验结果表明,与其他各种结构的Faster-RCNN网络模型相比,所提出的基于多特征尺度融合的改进Faster-RCNN算法能显著提高检测结果(F-score与原始Faster-RCNN相比提升了9.36%);与其他网络模型以及方法相比,所提出的基于多特征尺度融合的改进Faster-RCNN的自动检测精度明显更优。故所提出的基于多特征尺度融合的改进Faster-RCNN算法性能较优,能准确、有效地检测出眼底图像中的微动脉瘤。The presence of Microaneurysms(MAs)is the earliest detectable small abnormality of Diabetes Retinopathy(DR),a retinal disease that represents the leading cause of blindness among the middle-aged population globally.So the detection of MAs in fundus images is an important and challenging step for early diagnosis and prevention of critical health conditions.In particular,MAs are small vascular lesions consisting of swollen capillaries due to weakened vascular walls.Thus,retinal MAs can be associated with various ophthalmic and cardiovascular conditions.For instance,retinal MAs have been demonstrated as a risk factor for strokes.Therefore,it is crucial to detect the disease at its earliest stages to prevent its progression and consequent potential vision loss.However,MAs are a small target relative to the fundus image.Because the visual conditions are not ideal,MAs may present a low contrast with the background or may be affected by uneven illumination in the image.In addition,MAs may also be confused with other structures in the image,such as microbleeds,pigmentation changes,and dust particles in the fundus camera.Therefore,the automatic detection of MAs in fundus images is a significantly challenging task.The existing MA detection algorithm is difficult to achieve accurate detection of the lesion.Therefore,an improved faster-RCNN(Faster-RCNN-Pro)detection algorithm based on multi feature scale fusion is proposed.Firstly,the structure of feature extraction network and Region Proposal Network(RPN)are improved by using multi feature scale fusion to increase the utilization of micro target features;then,the quantization error introduced in the process of pooling the region of interest is eliminated by homogenizing and pooling the region of interest;finally,by redesigning the smooth L1 loss function in the loss function,a balanced L1 loss function is obtained to realize the optimization of the loss function,so as to effectively reduce the imbalance between large gradient difficult samples and small gradient easy sample

关 键 词：眼底图像 微动脉瘤 Faster-RCNN 多特征尺度融合 深度学习 

分 类 号：R318[医药卫生—生物医学工程] TP391.41[医药卫生—基础医学]