摘要
光学相干断层成像(OCT)广泛应用于眼科诊断与辅助治疗,但其成像质量不可避免地受到散斑噪声和运动伪影影响。本文提出了一种针对OCT超分辨率任务的多教师知识蒸馏网络MK-OCT,使用不同优势的教师网络训练平衡、轻量级和高效的学生网络。MK-OCT中高效通道蒸馏方法ECD的使用也使得模型能够更好地保留视网膜图像的纹理信息,满足临床需要。实验结果表明,与经典超分辨率网络相比,本文所提模型在重建精度和感知质量两个方面均表现优异,模型尺寸更小,计算量更少。
Optical coherence tomography(OCT)is widely used in ophthalmic diagnosis and adjuvant therapy,but its imaging quality is inevitably affected by speckle noise and motion artifacts.This article proposes a multi teacher knowledge distillation network MK-OCT for OCT super-resolution tasks,which uses teacher networks with different advantages to train balanced,lightweight,and efficient student networks.The use of efficient channel distillation method ECD in MK-OCT also enables the model to better preserve the texture information of retinal images,meeting clinical needs.The experimental results show that compared with classical super-resolution networks,the model proposed in this paper performs well in both reconstruction accuracy and perceptual quality,with smaller model size and less computational complexity.
作者
陈明惠
芦焱琦
杨文逸
王援柱
邵怡
Chen Minghui;Lu Yanqi;Yang Wenyi;Wang Yuanzhu;Shao Yi(Shanghai Engineering Research Center of Interventional Medical,Shanghai Institute for Interventional Medical Devices,School of Health Sciences and Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China;Shanghai Raykeen Laser Technology Co.,Ltd.,Shanghai 200120,China;Shanghai General Hospital,Shanghai 200080,China)
出处
《光电工程》
CAS
CSCD
北大核心
2024年第7期95-106,共12页
Opto-Electronic Engineering
基金
上海市科委产学研医项目(15DZ1940400)。
关键词
医学图像
光学相干断层图像
超分辨率
知识蒸馏
对比学习
medical images
optical coherence tomography images
super-resolution
knowledge distillation
contrastive learning
