基于多任务学习的眼底图像红色病变点分割
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作者简介:

郭松(1991-),男,博士,主要研究领域为医疗影像分析,深度学习.
康宏(1973-),男,博士,讲师,CCF专业会员,主要研究领域为数据库技术,机器学习.
李涛(1977-),男,博士,教授,博士生导师,CCF杰出会员,主要研究领域为异构计算,机器学习,智能物联网.
张玉军(1976-),男,博士,研究员,博士生导师,CCF高级会员,主要研究领域为计算机网络.
李宁(1994-),男,硕士,主要研究领域为医疗影像分析,深度学习.
王恺(1979-),男,博士,副教授,CCF专业会员,主要研究领域为人工智能,计算机视觉,机器学习.

通讯作者:

王恺,E-mail:wangk@nankai.edu.cn

中图分类号:

TP391

基金项目:

国家自然科学基金(61872200);国家重点研发计划(2016YFC0400709,2018YFB2100300);天津市自然科学基金(18YFYZCG00060,19JCZDJC31600);天津市教学成果奖重点培育项目(PYGJ-018)


Red Lesion Segmentation of Fundus Image with Multi-task Learning
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National Natural Science Foundation of China (61872200); National Key Research and Development Program of China (2016YFC0400709, 2018YFB2100300); Natural Science Foundation of Tianjin Municipality (18YFYZCG00060, 19JCZDJC316 00); Key Cultivation Project of Education Achievement Award of Tianjin Municipality (PYGJ-018)

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    摘要:

    糖尿病性视网膜病变(糖网病)是导致成年人视觉损失的主要因素之一.早期的眼底筛查可以显著降低这种视觉损失的可能性.彩色眼底图像由于具有采集便利、对人体无伤害等特点,常被用于大规模的眼底筛查工作.对眼底图像中的红色病变点而言,微动脉瘤是轻度非增殖性糖网病的主要标志,出血点与中度及重度非增殖性糖网病的诊断有关,因此,眼底图像中出血点和微动脉瘤的准确分割对糖网病分级诊断具有重要参考价值.提出一种基于多任务学习的分割模型Red-Seg来对出血点和微动脉瘤进行分割.该网络包含两个分支,每个分支处理一种病变点.设计了一种两阶段训练算法,并且两个阶段使用不同的损失函数:第1阶段使用改进的Top-k带权交叉熵损失函数,将模型训练集中在难分样本上;第2阶段将最小化假阳性和假阴性作为Red-Seg模型训练的优化目标,进一步减少病变点误分.最后,在IDRiD数据集上进行模型验证,并与其他病变点分割方法进行对比.实验结果表明,在应用Red-Seg模型进行微动脉瘤和出血点红色病变点分割时,两阶段训练算法可以显著减少病变点误分情况,尤其是出血点分割的准确率和召回率都提高2.8%.同时,与HED、FCRN、DeepLabv3+和L-Seg等图像级分割模型相比,Red-Seg模型在微动脉瘤分割上获得了更好的AUC_PR.

    Abstract:

    Diabetic retinopathy (DR) is the leading cause of vision loss for adult individuals, and early fundus screening can significantly reduce this visual loss. Color fundus image is often used in large-scale fundus screening due to the acquisition convenience and its human-harmless. As a kind of red lesions in fundus images, the appearance of microaneurysms is the main marker of mild non-proliferative DR, and hemorrhage, as another kind of red lesions, is related to moderate and severe non-proliferative DR. So that red lesions in fundus images are important indicators for the screening of DR. This study proposes a multi-task network, named Red-Seg, for red lesion segmentation. The network contains two individual branches, each is used for one kind of lesion segmentation. Meantime, a two-stage training algorithm is presented where different loss functions are used in different stages. In the first stage, modified Top-k balanced cross-entropy loss is used to push the network focuses on hard-to-classify samples. And, in the second stage, false positive and false negative are integrated as loss function into training to reduce misclassification further. At last, extensive experiments are employed on the IDRiD dataset, and the lesion segmentation results are compared with other methods. Experimental results show that proposed two-stage training algorithm can lead to much higher precision and recall, which means this method can reduce misclassification in some certain. Specifically for hemorrhage segmentation, both recall and precision increased by at least 2.8%. Meanwhile, compared with other image-level lesion segmentation models, such as HED, FCRN, DeepLabv3+, and L-Seg, Red-Seg achieves much higher AUC_PR on microaneurysm segmentation.

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郭松,李涛,李宁,康宏,张玉军,王恺.基于多任务学习的眼底图像红色病变点分割.软件学报,2021,32(11):3646-3658

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历史
  • 收稿日期:2019-07-18
  • 最后修改日期:2019-11-04
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  • 在线发布日期: 2021-11-05
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