C-SegNet:a practical approach for automated diabetic macular edema segmentation in optical coherence tomography imagesOA
Background:Diabetic macular edema is a prevalent retinal condition and a leading cause of visual impairment among diabetic patients’Early detection of affected areas is beneficial for effective diagnosis and treatment.Traditionally,diagnosis relies on optical coherence tomography imaging technology interpreted by ophthalmologists.However,this manual image interpretation is often slow and subjective.Therefore,developing automated segmentation for macular edema images is essential to enhance to improve the diagnosis efficiency and accuracy.Methods:In order to improve clinical diagnostic efficiency and accuracy,we proposed a SegNet network structure integrated with a convolutional block attention module(CBAM).This network introduces a multi-scale input module,the CBAM attention mechanism,and jump connection.The multi-scale input module enhances the network’s perceptual capabilities,while the lightweight CBAM effectively fuses relevant features across channels and spatial dimensions,allowing for better learning of varying information levels.Results:Experimental results demonstrate that the proposed network achieves an IoU of 80.127%and an accuracy of 99.162%.Compared to the traditional segmentation network,this model has fewer parameters,faster training and testing speed,and superior performance on semantic segmentation tasks,indicating its highly practical applicability.Conclusion:The C-SegNet proposed in this study enables accurate segmentation of Diabetic macular edema lesion images,which facilitates quicker diagnosis for healthcare professionals.
Zhi-Yuan Guan;Ge Deng;Shi-Long Shi;Zhen Tang;Xian-Kun Dong;Qiu-Yi Li;Shu-Jing Shen;Yong-Ling He;Xue-Jun Qiu
College of Health Science,Guangdong Pharmaceutical University,Guangzhou 510006,ChinaCollege of Health Science,Guangdong Pharmaceutical University,Guangzhou 510006,ChinaCollege of Health Science,Guangdong Pharmaceutical University,Guangzhou 510006,China Guangdong Provincial Engineering and Technology Research Center of Light and Health,Guangzhou 510006,ChinaCollege of Health Science,Guangdong Pharmaceutical University,Guangzhou 510006,ChinaCollege of Health Science,Guangdong Pharmaceutical University,Guangzhou 510006,ChinaCollege of Health Science,Guangdong Pharmaceutical University,Guangzhou 510006,ChinaDepartment of Rehabilitation Medicine,People’s Hospital of Yingde City Guangdong Province,Yingde 513000,ChinaCollege of Medical Information Engineering,Guangdong Pharmaceutical University,Guangzhou 510006,ChinaCollege of Health Science,Guangdong Pharmaceutical University,Guangzhou 510006,China Guangdong Provincial Engineering and Technology Research Center of Light and Health,Guangzhou 510006,China
医药卫生
multi-scale inputdiabetic macular edemaimage segmentationoptical coherence tomography
《Biomedical Engineering Communications》 2026 (2)
P.15-22,8
supported by the Guangdong Pharmaceutical University 2024 Higher Education Research Projects(GKP202403,GMP202402)the Guangdong Pharmaceutical University College Students’Innovation and Entrepreneurship Training Programs(Grant No.202504302033,202504302034,202504302036,and 202504302244).
评论