摘要
脉络膜的变化与很多眼科疾病密切相关。医生在诊断过程中常需要手动分割光学断层扫描图像(Optical Coherence Tomography,OCT)中的脉络膜,再定量分析脉络膜健康状况,但人工分割费时费力。脉络膜自动分割难点在于OCT图像中脉络膜下边界模糊,很难捕捉上下文信息,并且脉络膜结构跟视网膜结构比较类似,容易混淆。为了解决该难点,本文提出了融合坐标并行注意力模块和密集空洞卷积模块的残差编解码模型;设计了一种桥结构,包含了注意力机制和空洞卷积,在增加模型感受野的同时抑制浅层噪声;同时为了使模型关注脉络膜结构信息,引入了一种包含结构相似性的混合损失函数来训练模型。实验结果表明,该模型能有效提升对脉络膜的分割精度,在OCT脉络膜数据集上,Dice系数和Jaccard相似度达到了97.63%和95.28%。
Changes in the choroid are closely related to many ophthalmic diseases.Doctors often need to manually segment choroid in optical tomography image(OCT)during diagnosis,and then quantitatively analyze choroidal health.However,manual segmentation is time-consuming and laborious.The difficulty of automatic choroidal segmentation lies in the blurring of the lower choroidal boundary in the OCT images,which makes it difficult to capture context information.Morever,the choroidal structure is similar to the retina structure,which is easy to be confused.In order to solve this difficulty,this paper proposes a residual codec model that combines coordinate parallel attention module and dense hole convolution module.A bridge structure is designed,including attention mechanism and cavity convolution,which can increase the receptive field of the model while suppressing shallow noise.In order to make the model focus on choroidal structure information,a hybrid loss function containing structural similarity is introduced to train the model.It is shown from experimental results that this model can effectively improve the segmentation accuracy of choroid,and the similarity between Dice coefficient and Jaccard is 97.63%and 95.28%on the OCT choroid dataset.
作者
刘渝
夏源祥
万永菁
LIU Yu;XIA Yuanxiang;WAN Yongjing(School of Information Science and Engineering,East China University of Science and Technology,Shanghai 200237,China)
出处
《华东理工大学学报(自然科学版)》
CAS
CSCD
北大核心
2023年第2期247-254,共8页
Journal of East China University of Science and Technology
基金
国家自然科学基金(61872143)。
关键词
脉络膜分割
坐标并行注意力
密集空洞卷积
残差模型
混合损失函数
choroid segmentation
coordinate parallel attention
dense atrous convolution
residual model
hybrid loss function